Awesome-LLM-for-RecSys

• 2024.07.09 - Paper v6 released: Our camera-ready Version for TOIS, which will be archived.
• 2024.02.05 - Paper v5 released: New release with 27-page main content & more thorough taxonomies.
• 2023.06.29 - Paper v4 released: 7 papers have been newly added.
• 2023.06.28 - Paper v3 released: Fix typos.
• 2023.06.12 - Paper v2 released: Add summerization table in the appendix.
• 2023.06.09 - Paper v1 released: Initial version.

1.1.1 User- and Item-level Feature Augmentation | **Name** | **Paper** | **LLM Backbone (Largest)** | **LLM Tuning Strategy** | **Publication** | **Link** | |:---:|:---|:---:|:---:|:---:|:---:| | LLM4KGC | Knowledge Graph Completion Models are Few-shot Learners: An Empirical Study of Relation Labeling in E-commerce with LLMs | PaLM (540B)/ ChatGPT | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2305.09858v1) | | TagGPT | TagGPT: Large Language Models are Zero-shot Multimodal Taggers | ChatGPT | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2304.03022v1) | | ICPC | Large Language Models for User Interest Journeys | LaMDA (137B) | Full Finetuning/ Prompt Tuning | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2305.15498) | | KAR | Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models | ChatGPT | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2306.10933) | | PIE | Product Information Extraction using ChatGPT | ChatGPT | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2306.14921) | | LGIR | Enhancing Job Recommendation through LLM-based Generative Adversarial Networks | GhatGLM (6B) | Frozen | AAAI 2024 | [[Link]](https://arxiv.org/abs/2307.10747) | | GIRL | Generative Job Recommendations with Large Language Model | BELLE (7B) | Full Finetuning | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2307.02157) | | LLM-Rec | LLM-Rec: Personalized Recommendation via Prompting Large Language Models | text-davinci-003 | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2307.15780) | | HKFR | Heterogeneous Knowledge Fusion: A Novel Approach for Personalized Recommendation via LLM | ChatGPT | Frozen | RecSys 2023 | [[Link]](https://arxiv.org/abs/2308.03333) | | LLaMA-E | LLaMA-E: Empowering E-commerce Authoring with Multi-Aspect Instruction Following | LLaMA (30B) | LoRA | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2308.04913) | | EcomGPT | EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerce | BLOOMZ (7.1B) | Full Finetuning | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2308.06966) | | TF-DCon | Leveraging Large Language Models (LLMs) to Empower Training-Free Dataset Condensation for Content-Based Recommendation | ChatGPT | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2310.09874) | | RLMRec | Representation Learning with Large Language Models for Recommendation | ChatGPT | Frozen | WWW 2024 | [[Link]](https://arxiv.org/abs/2310.15950) | | LLMRec | LLMRec: Large Language Models with Graph Augmentation for Recommendation | ChatGPT | Frozen | WSDM 2024 | [[Link]](https://arxiv.org/pdf/2311.00423.pdf) | | LLMRG | Enhancing Recommender Systems with Large Language Model Reasoning Graphs | GPT4 | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2308.10835) | | CUP | Recommendations by Concise User Profiles from Review Text | ChatGPT | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2311.01314) | | SINGLE | Modeling User Viewing Flow using Large Language Models for Article Recommendation | ChatGPT | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2311.07619) | | SAGCN | Understanding Before Recommendation: Semantic Aspect-Aware Review Exploitation via Large Language Models | Vicuna (13B) | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2312.16275) | | UEM | User Embedding Model for Personalized Language Prompting | FLAN-T5-base (250M) | Full Finetuning | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2401.04858) | | LLMHG | LLM-Guided Multi-View Hypergraph Learning for Human-Centric Explainable Recommendation | GPT4 | Frozen | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2401.08217) | | Llama4Rec | Integrating Large Language Models into Recommendation via Mutual Augmentation and Adaptive Aggregation | LLaMA2 (7B) | Full Finetuning | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2401.13870) | | LLM4Vis | LLM4Vis: Explainable Visualization Recommendation using ChatGPT | ChatGPT | Frozen | EMNLP 2023 | [[Link]](https://arxiv.org/abs/2310.07652) | | LoRec | LoRec: Large Language Model for Robust Sequential Recommendation against Poisoning Attacks | LLaMA2 | Frozen | SIGIR 2024 | [[Link]](https://arxiv.org/abs/2401.17723) | 1.1.2 Instance-level Sample Generation | **Name** | **Paper** | **LLM Backbone (Largest)** | **LLM Tuning Strategy** | **Publication** | **Link** | |:---:|:---|:---:|:---:|:---:|:---:| | GReaT | Language Models are Realistic Tabular Data Generators | GPT2-medium (355M) | Full Finetuning | ICLR 2023 | [[Link]](https://arxiv.org/abs/2210.06280) | | ONCE | ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models | ChatGPT | Frozen | WSDM 2024 | [[Link]](https://arxiv.org/abs/2305.06566) | | AnyPredict | AnyPredict: Foundation Model for Tabular Prediction | ChatGPT | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2305.12081) | | DPLLM | Privacy-Preserving Recommender Systems with Synthetic Query Generation using Differentially Private Large Language Models | T5-XL (3B) | Full Finetuning | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2305.05973) | | MINT | Large Language Model Augmented Narrative Driven Recommendations | text-davinci-003 | Frozen | RecSys 2023 | [[Link]](https://arxiv.org/abs/2306.02250) | | Agent4Rec | On Generative Agents in Recommendation | ChatGPT | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2310.10108) | | RecPrompt | RecPrompt: A Prompt Tuning Framework for News Recommendation Using Large Language Models | GPT4 | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2312.10463) | | PO4ISR | Large Language Models for Intent-Driven Session Recommendations | ChatGPT | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2312.07552) | | BEQUE | Large Language Model based Long-tail Query Rewriting in Taobao Search | ChatGLM (6B) | FFT | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2311.03758) | | Agent4Ranking | Agent4Ranking: Semantic Robust Ranking via Personalized Query Rewriting Using Multi-agent LLM | ChatGPT | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2312.15450) | | PopNudge | Improving Conversational Recommendation Systems via Bias Analysis and Language-Model-Enhanced Data Augmentation | ChatGPT | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2310.16738) |

1.2.1 Representation Enhancement | **Name** | **Paper** | **LLM Backbone (Largest)** | **LLM Tuning Strategy** | **Publication** | **Link** | |:---:|:---|:---:|:---:|:---:|:---:| | U-BERT | U-BERT: Pre-training User Representations for Improved Recommendation | BERT-base (110M) | Full Finetuning | AAAI 2021 | [[Link]](https://ojs.aaai.org/index.php/AAAI/article/view/16557) | | UNBERT | UNBERT: User-News Matching BERT for News Recommendation | BERT-base (110M) | Full Finetuning | IJCAI 2021 | [[Link]](https://www.ijcai.org/proceedings/2021/462) | | PLM-NR | Empowering News Recommendation with Pre-trained Language Models | RoBERTa-base (125M) | Full Finetuning | SIGIR 2021 | [[Link]](https://arxiv.org/abs/2104.07413) | | Pyramid-ERNIE | Pre-trained Language Model based Ranking in Baidu Search | ERNIE (110M) | Full Finetuning | KDD 2021 | [[Link]](https://arxiv.org/abs/2105.11108) | | ERNIE-RS | Pre-trained Language Model for Web-scale Retrieval in Baidu Search | ERNIE (110M) | Full Finetuning | KDD 2021 | [[Link]](https://arxiv.org/abs/2106.03373) | | CTR-BERT | CTR-BERT: Cost-effective knowledge distillation for billion-parameter teacher models | Customized BERT (1.5B) | Full Finetuning | ENLSP 2021 | [[Link]](https://neurips2021-nlp.github.io/papers/20/CameraReady/camera_ready_final.pdf) | | SuKD | Learning Supplementary NLP Features for CTR Prediction in Sponsored Search | RoBERTa-large (355M) | Full Finetuning | KDD 2022 | [[Link]](https://dl.acm.org/doi/abs/10.1145/3534678.3539064) | | PREC | Boosting Deep CTR Prediction with a Plug-and-Play Pre-trainer for News Recommendation | BERT-base (110M) | Full Finetuning | COLING 2022 | [[Link]](https://aclanthology.org/2022.coling-1.249/) | | MM-Rec | MM-Rec: Visiolinguistic Model Empowered Multimodal News Recommendation | BERT-base (110M) | Full Finetuning | SIGIR 2022 | [[Link]](https://dl.acm.org/doi/abs/10.1145/3477495.3531896) | | Tiny-NewsRec | Tiny-NewsRec: Effective and Efficient PLM-based News Recommendation | UniLMv2-base (110M) | Full Finetuning | EMNLP 2022 | [[Link]](https://arxiv.org/abs/2112.00944) | | PLM4Tag | PTM4Tag: Sharpening Tag Recommendation of Stack Overflow Posts with Pre-trained Models | CodeBERT (125M) | Full Finetuning | ICPC 2022 | [[Link]](https://arxiv.org/abs/2203.10965) | | TwHIN-BERT | TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations | BERT-base (110M) | Full Finetuning | Arxiv 2022 | [[Link]](https://arxiv.org/abs/2209.07562) | | LSH | Improving Code Example Recommendations on Informal Documentation Using BERT and Query-Aware LSH: A Comparative Study | BERT-base (110M) | Full Finetuning | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2305.03017v1) | | LLM2BERT4Rec | Leveraging Large Language Models for Sequential Recommendation | text-embedding-ada-002 | Frozen | RecSys 2023 | [[Link]](https://arxiv.org/abs/2309.09261) | | LLM4ARec | Prompt Tuning Large Language Models on Personalized Aspect Extraction for Recommendations | GPT2 (110M) | Prompt Tuning | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2306.01475) | | TIGER | Recommender Systems with Generative Retrieval | Sentence-T5-base (223M) | Frozen | NIPS 2023 | [[Link]](https://arxiv.org/abs/2305.05065) | | TBIN | TBIN: Modeling Long Textual Behavior Data for CTR Prediction | BERT-base (110M) | Frozen | DLP-RecSys 2023 | [[Link]](https://arxiv.org/abs/2308.08483) | | LKPNR | LKPNR: LLM and KG for Personalized News Recommendation Framework | LLaMA2 (7B) | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2308.12028) | | SSNA | Towards Efficient and Effective Adaptation of Large Language Models for Sequential Recommendation | DistilRoBERTa-base (83M) | Layerwise Adapter Tuning | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2310.01612) | | CollabContext | Collaborative Contextualization: Bridging the Gap between Collaborative Filtering and Pre-trained Language Model | Instructor-XL (1.5B) | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2310.09400) | | LMIndexer | Language Models As Semantic Indexers | T5-base (223M) | Full Finetuning | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2310.07815) | | Stack | A BERT based Ensemble Approach for Sentiment Classification of Customer Reviews and its Application to Nudge Marketing in e-Commerce | BERT-base (110M) | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2311.10782) | | N/A | Utilizing Language Models for Tour Itinerary Recommendation | BERT-base (110M) | Full Finetuning | PMAI@IJCAI 2023 | [[Link]](https://arxiv.org/abs/2311.12355) | | UEM | User Embedding Model for Personalized Language Prompting | Sentence-T5-base (223M) | Frozen | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2401.04858) | | Social-LLM | Social-LLM: Modeling User Behavior at Scale using Language Models and Social Network Data | SBERT-MPNet-base (110M) | Frozen | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2401.00893) | | LLMRS | LLMRS: Unlocking Potentials of LLM-Based Recommender Systems for Software Purchase | MPNet (110M) | Frozen | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2401.06676) | | KERL | Knowledge Graphs and Pre-trained Language Models enhanced Representation Learning for Conversational Recommender Systems | BERT-mini | Frozen | TNNLS | [[Link]](https://arxiv.org/abs/2312.10967) | | N/A | Empowering Few-Shot Recommender Systems with Large Language Models -- Enhanced Representations | ChatGPT | Frozen | IEEE Access | [[Link]](https://arxiv.org/abs/2312.13557) | 1.2.2 Unified Cross-domain Recommendation | **Name** | **Paper** | **LLM Backbone (Largest)** | **LLM Tuning Strategy** | **Publication** | **Link** | |:---:|:---|:---:|:---:|:---:|:---:| | ZESRec | Zero-Shot Recommender Systems | BERT-base (110M) | Frozen | Arxiv 2021 | [[Link]](https://arxiv.org/abs/2105.08318) | | UniSRec | Towards Universal Sequence Representation Learning for Recommender Systems | BERT-base (110M) | Frozen | KDD 2022 | [[Link]](https://arxiv.org/abs/2206.05941) | | TransRec | TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback | BERT-base (110M) | Full Finetuning | Arxiv 2022 | [[Link]](https://arxiv.org/abs/2206.06190) | | VQ-Rec | Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders | BERT-base (110M) | Frozen | WWW 2023 | [[Link]](https://arxiv.org/abs/2210.12316) | | IDRec vs MoRec | Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisited | BERT-base (110M) | Full Finetuning | SIGIR 2023 | [[Link]](https://arxiv.org/abs/2303.13835) | | TransRec | Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights | RoBERTa-base (125M) | Layerwise Adapter Tuning | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2305.15036) | | TCF | Exploring the Upper Limits of Text-Based Collaborative Filtering Using Large Language Models: Discoveries and Insights | OPT-175B (175B) | Frozen/ Full Finetuning | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2305.11700) | | S&R Foundation | An Unified Search and Recommendation Foundation Model for Cold-Start Scenario | ChatGLM (6B) | Frozen | CIKM 2023 | [[Link]](https://arxiv.org/abs/2309.08939) | | MISSRec | MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation | CLIP-B/32 (400M) | Full Finetuning | MM 2023 | [[Link]](https://arxiv.org/abs/2308.11175) | | UFIN | UFIN: Universal Feature Interaction Network for Multi-Domain Click-Through Rate Prediction | FLAN-T5-base (250M) | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2311.15493) | | PMMRec | Multi-Modality is All You Need for Transferable Recommender Systems | RoBERTa-large (355M) | Top-2-layer Finetuning | ICDE 2024 | [[Link]](https://arxiv.org/abs/2312.09602) | | Uni-CTR | A Unified Framework for Multi-Domain CTR Prediction via Large Language Models | Sheared-LLaMA (1.3B) | LoRA | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2312.10743) | | PCDR | Prompt-enhanced Federated Content Representation Learning for Cross-domain Recommendation | BERT-base (110M) | Frozen | WWW 2024 | [[Link]](https://arxiv.org/abs/2401.14678) |

1.3.1 Item Scoring Task | **Name** | **Paper** | **LLM Backbone (Largest)** | **LLM Tuning Strategy** | **Publication** | **Link** | |:---:|:---|:---:|:---:|:---:|:---:| | LMRecSys | Language Models as Recommender Systems: Evaluations and Limitations | GPT2-XL (1.5B) | Full Finetuning | ICBINB 2021 | [[Link]](https://openreview.net/forum?id=hFx3fY7-m9b) | | PTab | PTab: Using the Pre-trained Language Model for Modeling Tabular Data | BERT-base (110M) | Full Finetuning | Arxiv 2022 | [[Link]](https://arxiv.org/abs/2209.08060) | | UniTRec | UniTRec: A Unified Text-to-Text Transformer and Joint Contrastive Learning Framework for Text-based Recommendation | BART (406M) | Full Finetuning | ACL 2023 | [[Link]](https://arxiv.org/abs/2305.15756) | | Prompt4NR | Prompt Learning for News Recommendation | BERT-base (110M) | Full Finetuning | SIGIR 2023 | [[Link]](https://arxiv.org/abs/2304.05263) | | RecFormer | Text Is All You Need: Learning Language Representations for Sequential Recommendation | LongFormer (149M) | Full Finetuning | KDD 2023 | [[Link]](https://arxiv.org/abs/2305.13731v1) | | TabLLM | TabLLM: Few-shot Classification of Tabular Data with Large Language Models | T0 (11B) | Few-shot Parameter-effiecnt Finetuning | AISTATS 2023 | [[Link]](https://arxiv.org/abs/2210.10723) | | Zero-shot GPT | Zero-Shot Recommendation as Language Modeling | GPT2-medium (355M) | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2112.04184) | | FLAN-T5 | Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction | FLAN-5-XXL (11B) | Full Finetuning | Arxiv 2023 | [[Link]](https://arxiv.org/pdf/2305.06474.pdf) | | BookGPT | BookGPT: A General Framework for Book Recommendation Empowered by Large Language Model | ChatGPT | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2305.15673v1) | | TALLRec | TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation | LLaMA (7B) | LoRA | RecSys 2023 | [[Link]](https://arxiv.org/abs/2305.00447) | | PBNR | PBNR: Prompt-based News Recommender System | T5-small (60M) | Full Finetuning | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2304.07862) | | CR-SoRec | CR-SoRec: BERT driven Consistency Regularization for Social Recommendation | BERT-base (110M) | Full Finetuning | RecSys 2023 | [[Link]](https://dl.acm.org/doi/fullHtml/10.1145/3604915.3608844) | | PromptRec | Towards Personalized Cold-Start Recommendation with Prompts | LLaMA (7B) | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2306.17256) | | GLRec | Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations | BELLE-LLaMA (7B) | LoRA | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2307.05722) | | BERT4CTR | BERT4CTR: An Efficient Framework to Combine Pre-trained Language Model with Non-textual Features for CTR Prediction | RoBERTa-large (355M) | Full Finetuning | KDD 2023 | [[Link]](https://dl.acm.org/doi/abs/10.1145/3580305.3599780) | | ReLLa | ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation | Vicuna (13B) | LoRA | WWW 2024 | [[Link]](https://arxiv.org/abs/2308.11131) | | TASTE | Text Matching Improves Sequential Recommendation by Reducing Popularity Biases | T5-base (223M) | Full Finetuning | CIKM 2023 | [[Link]](https://arxiv.org/abs/2308.14029) | | N/A | Unveiling Challenging Cases in Text-based Recommender Systems | BERT-base (110M) | Full Finetuning | RecSys Workshop 2023 | [[Link]](https://ceur-ws.org/Vol-3476/paper5.pdf) | | ClickPrompt | ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction | RoBERTa-large (355M) | Full Finetuning | WWW 2024 | [[Link]](https://arxiv.org/abs/2310.09234) | | SetwiseRank | A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models | FLAN-T5-XXL (11B) | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2310.09497) | | UPSR | Thoroughly Modeling Multi-domain Pre-trained Recommendation as Language | T5-base (223M) | Full Finetuning | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2310.13540) | | LLM-Rec | One Model for All: Large Language Models are Domain-Agnostic Recommendation Systems | OPT (6.7B) | LoRA | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2310.14304) | | LLMRanker | Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels | FLAN PaLM2 S | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2310.14122) | | CoLLM | CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendation | Vicuna (7B) | LoRA | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2310.19488) | | FLIP | FLIP: Towards Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Prediction | RoBERTa-large (355M) | Full Finetuning | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2310.19453) | | BTRec | BTRec: BERT-Based Trajectory Recommendation for Personalized Tours | BERT-base (110M) | Full Finetuning | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2310.19886) | | CLLM4Rec | Collaborative Large Language Model for Recommender Systems | GPT2 (110M) | Full Finetuning | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2311.01343) | | CUP | Recommendations by Concise User Profiles from Review Text | BERT-base (110M) | Last-layer Finetuning | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2311.01314) | | N/A | Instruction Distillation Makes Large Language Models Efficient Zero-shot Rankers | FLAN-T5-XL (3B) | Full Finetuning | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2311.01555) | | CoWPiRec | Collaborative Word-based Pre-trained Item Representation for Transferable Recommendation | BERT-base (110M) | Full Finetuning | ICDM 2023 | [[Link]](https://arxiv.org/abs/2311.10501) | | RecExplainer | RecExplainer: Aligning Large Language Models for Recommendation Model Interpretability | Vicuna-v1.3 (7B) | LoRA | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2311.10947) | | E4SRec | E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential Recommendation | LLaMA2 (13B) | LoRA | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2312.02443) | | CER | The Problem of Coherence in Natural Language Explanations of Recommendations | GPT2 (110M) | Full Finetuning | ECAI 2023 | [[Link]](https://arxiv.org/abs/2312.11356) | | LSAT | Preliminary Study on Incremental Learning for Large Language Model-based Recommender Systems | LLaMA (7B) | LoRA | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2312.15599) | | Llama4Rec | Integrating Large Language Models into Recommendation via Mutual Augmentation and Adaptive Aggregation | LLaMA2 (7B) | Full Finetuning | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2401.13870) | 1.3.2 Item Generation Task | **Name** | **Paper** | **LLM Backbone (Largest)** | **LLM Tuning Strategy** | **Publication** | **Link** | |:---:|:---|:---:|:---:|:---:|:---:| | GPT4Rec | GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretation | GPT2 (110M) | Full Finetuning | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2304.03879) | | VIP5 | VIP5: Towards Multimodal Foundation Models for Recommendation | T5-base (223M) | Layerwise Adater Tuning | EMNLP 2023 | [[Link]](https://arxiv.org/abs/2305.14302) | | P5-ID | How to Index Item IDs for Recommendation Foundation Models | T5-small (60M) | Full Finetuning | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2305.06569) | | FaiRLLM | Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation | ChatGPT | Frozen | RecSys 2023 | [[Link]](https://arxiv.org/abs/2305.07609) | | PALR | PALR: Personalization Aware LLMs for Recommendation | LLaMA (7B) | Full Finetuning | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2305.07622) | | ChatGPT | Large Language Models are Zero-Shot Rankers for Recommender Systems | ChatGPT | Frozen | ECIR 2024 | [[Link]](https://arxiv.org/abs/2305.08845) | | AGR | Sparks of Artificial General Recommender (AGR): Early Experiments with ChatGPT | ChatGPT | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2305.04518) | | NIR | Zero-Shot Next-Item Recommendation using Large Pretrained Language Models | GPT3 (175B) | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2304.03153) | | GPTRec | Generative Sequential Recommendation with GPTRec | GPT2-medium (355M) | Full Finetuning | Gen-IR@SIGIR 2023 | [[Link]](https://arxiv.org/abs/2306.11114) | | ChatNews | A Preliminary Study of ChatGPT on News Recommendation: Personalization, Provider Fairness, Fake News | ChatGPT | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2306.10702) | | N/A | Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences | PaLM (62B) | Frozen | RecSys 2023 | [[Link]](https://arxiv.org/abs/2307.14225) | | LLMSeqPrompt | Leveraging Large Language Models for Sequential Recommendation | OpenAI ada model | Finetune | RecSys 2023 | [[Link]](https://arxiv.org/abs/2309.09261) | | GenRec | GenRec: Large Language Model for Generative Recommendation | LLaMA (7B) | LoRA | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2307.00457) | | UP5 | UP5: Unbiased Foundation Model for Fairness-aware Recommendation | T5-base (223M) | Prefix Tuning | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2305.12090) | | HKFR | Heterogeneous Knowledge Fusion: A Novel Approach for Personalized Recommendation via LLM | ChatGLM (6B) | LoRA | RecSys 2023 | [[Link]](https://arxiv.org/abs/2308.03333) | | N/A | The Unequal Opportunities of Large Language Models: Revealing Demographic Bias through Job Recommendations | ChatGPT | Frozen | EAAMO 2023 | [[Link]](https://arxiv.org/abs/2308.02053) | | BIGRec | A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems | LLaMA (7B) | LoRA | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2308.08434) | | KP4SR | Knowledge Prompt-tuning for Sequential Recommendation | T5-small (60M) | Full Finetuning | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2308.08459) | | RecSysLLM | Leveraging Large Language Models for Pre-trained Recommender Systems | GLM (10B) | LoRA | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2308.10837) | | POD | Prompt Distillation for Efficient LLM-based Recommendation | T5-small (60M) | Full Finetuning | CIKM 2023 | [[Link]](https://lileipisces.github.io/files/CIKM23-POD-paper.pdf) | | N/A | Evaluating ChatGPT as a Recommender System: A Rigorous Approach | ChatGPT | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2309.03613) | | RaRS | Retrieval-augmented Recommender System: Enhancing Recommender Systems with Large Language Models | ChatGPT | Frozen | RecSys Doctoral Symposium 2023 | [[Link]](https://dl.acm.org/doi/abs/10.1145/3604915.3608889) | | JobRecoGPT | JobRecoGPT -- Explainable job recommendations using LLMs | GPT4 | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2309.11805) | | LANCER | Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language Modeling | GPT2 (110M) | Prefix Tuning | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2309.10435) | | TransRec | A Multi-facet Paradigm to Bridge Large Language Model and Recommendation | LLaMA (7B) | LoRA | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2310.06491) | | AgentCF | AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems | text-davinci-003 & gpt-3.5-turbo | Frozen | WWW 2024 | [[Link]](https://arxiv.org/abs/2310.09233) | | P4LM | Factual and Personalized Recommendations using Language Models and Reinforcement Learning | PaLM2-XS | Full Finetuning | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2310.06176) | | InstructMK | Multiple Key-value Strategy in Recommendation Systems Incorporating Large Language Model | LLaMA (7B) | Full Finetuning | CIKM GenRec 2023 | [[Link]](https://arxiv.org/abs/2310.16409) | | LightLM | LightLM: A Lightweight Deep and Narrow Language Model for Generative Recommendation | T5-small (60M) | Full Finetuning | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2310.17488) | | LlamaRec | LlamaRec: Two-Stage Recommendation using Large Language Models for Ranking | LLaMA2 (7B) | QLoRA | PGAI@CIKM 2023 | [[Link]](https://arxiv.org/abs/2311.02089) | | N/A | Exploring Recommendation Capabilities of GPT-4V(ision): A Preliminary Case Study | GPT-4V | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2311.04199) | | N/A | Exploring Fine-tuning ChatGPT for News Recommendation | ChatGPT | gpt-3.5-turbo finetuning API | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2311.05850) | | N/A | Do LLMs Implicitly Exhibit User Discrimination in Recommendation? An Empirical Study | ChatGPT | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2311.07054) | | LC-Rec | Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation | LLaMA (7B) | LoRA | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2311.09049) | | DOKE | Knowledge Plugins: Enhancing Large Language Models for Domain-Specific Recommendations | ChatGPT | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2311.10779) | | ControlRec | ControlRec: Bridging the Semantic Gap between Language Model and Personalized Recommendation | T5-base (223M) | Full Finetuning | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2311.16441) | | LLaRA | LLaRA: Large Language-Recommendation Assistant | LLaMA2 (7B) | LoRA | SIGIR 2024 | [[Link]](https://arxiv.org/abs/2312.02445) | | PO4ISR | Large Language Models for Intent-Driven Session Recommendations | ChatGPT | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2312.07552) | | DRDT | DRDT: Dynamic Reflection with Divergent Thinking for LLM-based Sequential Recommendation | ChatGPT | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2312.11336) | | RecPrompt | RecPrompt: A Prompt Tuning Framework for News Recommendation Using Large Language Models | GPT4 | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2312.10463) | | LiT5 | Scaling Down, LiTting Up: Efficient Zero-Shot Listwise Reranking with Seq2seq Encoder-Decoder Models | T5-XL (3B) | Full Finetuning | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2312.16098) | | STELLA | Large Language Models are Not Stable Recommender Systems | ChatGPT | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2312.15746) | | Llama4Rec | Integrating Large Language Models into Recommendation via Mutual Augmentation and Adaptive Aggregation | LLaMA2 (7B) | Full Finetuning | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2401.13870) | | RECLLM | Understanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recency | ChatGPT | Frozen | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2401.10545) | | DEALRec | Data-efficient Fine-tuning for LLM-based Recommendation | LLaMA (7B) | LoRA | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2401.17197) | 1.3.3 Hybrid Task | **Name** | **Paper** | **LLM Backbone (Largest)** | **LLM Tuning Strategy** | **Publication** | **Link** | |:---:|:---|:---:|:---:|:---:|:---:| | P5 | Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5) | T5-base (223M) | Full Finetuning | RecSys 2022 | [[Link]](https://arxiv.org/abs/2203.13366) | | M6-Rec | M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems | M6-base (300M) | Option Tuning | Arxiv 2022 | [[Link]](https://arxiv.org/abs/2205.08084) | | InstructRec | Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach | FLAN-T5-XL (3B) | Full Finetuning | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2305.07001) | | ChatGPT | Is ChatGPT a Good Recommender? A Preliminary Study | ChatGPT | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2304.10149) | | ChatGPT | Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agent | ChatGPT | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2304.09542) | | ChatGPT | Uncovering ChatGPT's Capabilities in Recommender Systems | ChatGPT | Frozen | RecSys 2023 | [[Link]](https://arxiv.org/abs/2305.02182) | | BDLM | Bridging the Information Gap Between Domain-Specific Model and General LLM for Personalized Recommendation | Vicuna (7B) | Full Finetuning | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2311.03778) | | RecRanker | RecRanker: Instruction Tuning Large Language Model as Ranker for Top-k Recommendation | LLaMA2 (13B) | Full Finetuning | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2312.16018) |

1.4.1 Task-oriented User Interaction | **Name** | **Paper** | **LLM Backbone (Largest)** | **LLM Tuning Strategy** | **Publication** | **Link** | |:---:|:---|:---:|:---:|:---:|:---:| | TG-ReDial | Towards Topic-Guided Conversational Recommender System | BERT-base (110M) & GPT2 (110M) | Unknown | COLING 2020 | [[Link]](https://arxiv.org/abs/2010.04125) | | TCP | Follow Me: Conversation Planning for Target-driven Recommendation Dialogue Systems | BERT-base (110M) | Full Finetuning | Arxiv 2022 | [[Link]](https://arxiv.org/abs/2208.03516) | | MESE | Improving Conversational Recommendation Systems' Quality with Context-Aware Item Meta-Information | DistilBERT (67M) & GPT2 (110M) | Full Finetuning | ACL 2022 | [[Link]](https://arxiv.org/abs/2112.08140) | | UniMIND | A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systems | BART-base (139M) | Full Finetuning | ACM TOIS 2023 | [[Link]](https://arxiv.org/abs/2204.06923) | | VRICR | Variational Reasoning over Incomplete Knowledge Graphs for Conversational Recommendation | BERT-base (110M) | Full Finetuning | WSDM 2023 | [[Link]](https://arxiv.org/abs/2212.11868) | | KECR | Explicit Knowledge Graph Reasoning for Conversational Recommendation | BERT-base (110M) & GPT2 (110M) | Frozen | ACM TIST 2023 | [[Link]](https://arxiv.org/abs/2305.00783) | | N/A | Large Language Models as Zero-Shot Conversational Recommenders | GPT4 | Frozen | CIKM 2023 | [[Link]](https://arxiv.org/abs/2308.10053) | | MuseChat | MuseChat: A Conversational Music Recommendation System for Videos | Vicuna (7B) | LoRA | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2310.06282) | | N/A | Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales Dialogue | Chinese-Alpaca (7B) | LoRA | EMNLP 2023 Findings | [[Link]](https://arxiv.org/abs/2310.14626) | | N/A | ChatGPT for Conversational Recommendation: Refining Recommendations by Reprompting with Feedback | ChatGPT | Frozen | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2401.03605) | 1.4.2 Open-ended User Interaction | **Name** | **Paper** | **LLM Backbone (Largest)** | **LLM Tuning Strategy** | **Publication** | **Link** | |:---:|:---|:---:|:---:|:---:|:---:| | BARCOR | BARCOR: Towards A Unified Framework for Conversational Recommendation Systems | BART-base (139M) | Selective-layer Finetuning | Arxiv 2022 | [[Link]](https://arxiv.org/abs/2203.14257) | | RecInDial | RecInDial: A Unified Framework for Conversational Recommendation with Pretrained Language Models | DialoGPT (110M) | Full Finetuning | AACL 2022 | [[Link]](https://arxiv.org/abs/2110.07477) | | UniCRS | Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning | DialoGPT-small (176M) | Frozen | KDD 2022 | [[Link]](https://arxiv.org/abs/2206.09363) | | T5-CR | Multi-Task End-to-End Training Improves Conversational Recommendation | T5-base (223M) | Full Finetuning | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2305.06218) | | TtW | Talk the Walk: Synthetic Data Generation for Conversational Music Recommendation | T5-base (223M) & T5-XXL (11B) | Full Finetuning & Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2301.11489) | | N/A | Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models | ChatGPT | Frozen | EMNLP 2023 | [[Link]](https://arxiv.org/abs/2305.13112) | | PECRS | Parameter-Efficient Conversational Recommender System as a Language Processing Task | GPT2-medium (355M) | LoRA | EACL 2024 | [[Link]](https://arxiv.org/abs/2401.14194) |

| **Name** | **Paper** | **LLM Backbone (Largest)** | **LLM Tuning Strategy** | **Publication** | **Link** | |:---:|:---|:---:|:---:|:---:|:---:| | Chat-REC | Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System | ChatGPT | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2303.14524) | | RecLLM | Leveraging Large Language Models in Conversational Recommender Systems | LLaMA (7B) | Full Finetuning | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2305.07961) | | RAH | RAH! RecSys-Assistant-Human: A Human-Central Recommendation Framework with Large Language Models | GPT4 | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2308.09904) | | RecMind | RecMind: Large Language Model Powered Agent For Recommendation | ChatGPT | Frozen | NAACL 2024 | [[Link]](https://arxiv.org/abs/2308.14296) | | InteRecAgent | Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations | GPT4 | Frozen | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2308.16505) | | CORE | Lending Interaction Wings to Recommender Systems with Conversational Agents | N/A | N/A | NIPS 2023 | [[Link]](https://arxiv.org/abs/2310.04230) | | LLMCRS | A Large Language Model Enhanced Conversational Recommender System | LLaMA (7B) | Full Finetuning | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2308.06212) |

| **Paper** | **Publication** | **Link** | |:---|:---:|:---:| | All Roads Lead to Rome: Unveiling the Trajectory of Recommender Systems Across the LLM Era | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2407.10081) | | Survey for Landing Generative AI in Social and E-commerce Recsys - the Industry Perspectives | Arxiv 2024 | [[Link]](https://www.arxiv.org/abs/2406.06475) | | A Survey of Generative Search and Recommendation in the Era of Large Language Models | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2404.16924) | | When Search Engine Services meet Large Language Models: Visions and Challenges | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2407.00128) | | A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys) | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2404.00579) | | Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.18590) | | Foundation Models for Recommender Systems: A Survey and New Perspectives | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.11143) | | Prompting Large Language Models for Recommender Systems: A Comprehensive Framework and Empirical Analysis | Arixv 2024 | [[Link]](https://arxiv.org/abs/2401.04997) | | User Modeling in the Era of Large Language Models: Current Research and Future Directions | IEEE Data Engineering Bulletin 2023 | [[Link]](https://arxiv.org/abs/2312.11518) | | A Survey on Large Language Models for Personalized and Explainable Recommendations | Arxiv 2023 |[[Link]](https://arxiv.org/abs/2311.12338) | | Large Language Models for Generative Recommendation: A Survey and Visionary Discussions | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2309.01157) | | Large Language Models for Information Retrieval: A Survey | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2308.07107) | | When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2307.16376) | | | Recommender Systems in the Era of Large Language Models (LLMs) | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2307.02046) | | A Survey on Large Language Models for Recommendation | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2305.19860) | | Pre-train, Prompt and Recommendation: A Comprehensive Survey of Language Modelling Paradigm Adaptations in Recommender Systems | TACL 2023 | [[Link]](https://arxiv.org/abs/2302.03735) | | Self-Supervised Learning for Recommender Systems: A Survey | TKDE 2022 | [[Link]](https://arxiv.org/abs/2203.15876) |

| **Paper** | **Publication** | **Link** | |:---|:---:|:---:| | Large Language Model Can Interpret Latent Space of Sequential Recommender | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2310.20487) | | Zero-Shot Recommendations with Pre-Trained Large Language Models for Multimodal Nudging | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2309.01026) | | INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2401.06532) | | Evaluation of Synthetic Datasets for Conversational Recommender Systems | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2212.08167v1) | | Generative Recommendation: Towards Next-generation Recommender Paradigm | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2304.03516) | | Towards Personalized Prompt-Model Retrieval for Generative Recommendation | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2308.02205) | | Generative Next-Basket Recommendation | RecSys 2023 | [[Link]](https://dl.acm.org/doi/abs/10.1145/3604915.3608823) | | Unlocking the Potential of Large Language Models for Explainable Recommendations | LLaMA (7B) | LoRA | Arxiv 2023 | [[Link]](https://arxiv.org/abs/2312.15661) | | Logic-Scaffolding: Personalized Aspect-Instructed Recommendation Explanation Generation using LLMs | Falcon (40B) | Frozen | WSDM 2024 | [[Link]](https://arxiv.org/abs/2312.14345) | | Improving Sequential Recommendations with LLMs | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.01339) | | A Multi-Agent Conversational Recommender System | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.01135) | | TransFR: Transferable Federated Recommendation with Pre-trained Language Models | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.01124) | | Large Language Model Distilling Medication Recommendation Model | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.02803) | | Uncertainty-Aware Explainable Recommendation with Large Language Models | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.03366) | | Natural Language User Profiles for Transparent and Scrutable Recommendations | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.05810) | | Leveraging LLMs for Unsupervised Dense Retriever Ranking | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.04853) | | RA-Rec: An Efficient ID Representation Alignment Framework for LLM-based Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.04527) | | A Multi-Agent Conversational Recommender System | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.01135) | | Fairly Evaluating Large Language Model-based Recommendation Needs Revisit the Cross-Entropy Loss | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.06216) | | SearchAgent: A Lightweight Collaborative Search Agent with Large Language Models | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.06360) | | Large Language Model Interaction Simulator for Cold-Start Item Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.09176) | | Enhancing ID and Text Fusion via Alternative Training in Session-based Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.08921) | | eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.08831) | | LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.09617) | | LLM-based Federated Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.09959) | | Rethinking Large Language Model Architectures for Sequential Recommendations | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.09543) | | Large Language Model with Graph Convolution for Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.08859) | | Rec-GPT4V: Multimodal Recommendation with Large Vision-Language Models | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.08670) | | Enhancing Recommendation Diversity by Re-ranking with Large Language Models | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2401.11506) | | Are ID Embeddings Necessary? Whitening Pre-trained Text Embeddings for Effective Sequential Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.10602) | | SPAR: Personalized Content-Based Recommendation via Long Engagement Attention | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.10555) | | Cognitive Personalized Search Integrating Large Language Models with an Efficient Memory Mechanism | WWW 2024 | [[Link]](https://arxiv.org/abs/2402.10548) | | Large Language Models as Data Augmenters for Cold-Start Item Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.11724) | | Explain then Rank: Scale Calibration of Neural Rankers Using Natural Language Explanations from Large Language Models | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.12276) | | LLM4SBR: A Lightweight and Effective Framework for Integrating Large Language Models in Session-based Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.13840) | | Breaking the Barrier: Utilizing Large Language Models for Industrial Recommendation Systems through an Inferential Knowledge Graph | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.13750) | | User-LLM: Efficient LLM Contextualization with User Embeddings | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.13598) | | Stealthy Attack on Large Language Model based Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.14836) | | Multi-Agent Collaboration Framework for Recommender Systems | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.15235) | | Item-side Fairness of Large Language Model-based Recommendation System | WWW 2024 | [[Link]](https://arxiv.org/abs/2402.15215) | | Integrating Large Language Models with Graphical Session-Based Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.16539) | | Language-Based User Profiles for Recommendation | LLM-IGS@WSDM2024 | [[Link]](https://arxiv.org/abs/2402.15623) | | BASES: Large-scale Web Search User Simulation with Large Language Model based Agents | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.17505) | | Prospect Personalized Recommendation on Large Language Model-based Agent Platform | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.18240) | | Sequence-level Semantic Representation Fusion for Recommender Systems | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.18166) | | Corpus-Steered Query Expansion with Large Language Models | ECAL 2024 | [[Link]](https://arxiv.org/abs/2402.18031) | | NoteLLM: A Retrievable Large Language Model for Note Recommendation | WWW 2024 | [[Link]](https://arxiv.org/abs/2403.01744) | | An Interpretable Ensemble of Graph and Language Models for Improving Search Relevance in E-Commerce | WWW 2024 | [[Link]](https://arxiv.org/abs/2403.00923) | | LLM-Ensemble: Optimal Large Language Model Ensemble Method for E-commerce Product Attribute Value Extraction | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2403.00863) | | Enhancing Long-Term Recommendation with Bi-level Learnable Large Language Model Planning | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2403.00843) | | InteraRec: Interactive Recommendations Using Multimodal Large Language Models | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2403.00822) | | ChatDiet: Empowering Personalized Nutrition-Oriented Food Recommender Chatbots through an LLM-Augmented Framework | CHASE 2024 | [[Link]](https://arxiv.org/abs/2403.00781) | | Towards Efficient and Effective Unlearning of Large Language Models for Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2403.03536) | | Generative News Recommendation | WWW 2024 | [[Link]](https://arxiv.org/abs/2403.03424) | | Bridging Language and Items for Retrieval and Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2403.03952) | | Can Small Language Models be Good Reasoners for Sequential Recommendation? | WWW 2024 | [[Link]](https://arxiv.org/abs/2403.04260) | | Aligning Large Language Models for Controllable Recommendations | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2403.05063) | | Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks | WWW 2024 | [[Link]](https://arxiv.org/abs/2403.05185) | | Towards Graph Foundation Models for Personalization | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2403.07478) | | CFaiRLLM: Consumer Fairness Evaluation in Large-Language Model Recommender System | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2403.05668) | | CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2403.06447) | | RecAI: Leveraging Large Language Models for Next-Generation Recommender Systems | WWW 2024 Demo | [[Link]](https://arxiv.org/pdf/2403.06465.pdf) | | KELLMRec: Knowledge-Enhanced Large Language Models for Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2403.06642) | | USimAgent: Large Language Models for Simulating Search Users | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2403.09142) | | CALRec: Contrastive Alignment of Generative LLMs For Sequential Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2405.02429) | | Integrating Large Language Models with Graphical Session-Based Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.16539) | | Language-Based User Profiles for Recommendation | LLM-IGS@WSDM2024 | [[Link]](https://arxiv.org/abs/2402.15623) | | BASES: Large-scale Web Search User Simulation with Large Language Model based Agents | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.17505) | | Prospect Personalized Recommendation on Large Language Model-based Agent Platform | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.18240) | | Sequence-level Semantic Representation Fusion for Recommender Systems | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2402.18166) | | Corpus-Steered Query Expansion with Large Language Models | EACL 2024 | [[Link]](https://arxiv.org/abs/2402.18031) | | NoteLLM: A Retrievable Large Language Model for Note Recommendation | WWW 2024 | [[Link]](https://arxiv.org/abs/2403.01744) | | An Interpretable Ensemble of Graph and Language Models for Improving Search Relevance in E-Commerce | WWW 2024 | [[Link]](https://arxiv.org/abs/2403.00923) | | LLM-Ensemble: Optimal Large Language Model Ensemble Method for E-commerce Product Attribute Value Extraction | SIGIR 2024 | [[Link]](https://arxiv.org/abs/2403.00863) | | Enhancing Long-Term Recommendation with Bi-level Learnable Large Language Model Planning | SIGIR 2024 | [[Link]](https://arxiv.org/abs/2403.00843) | | Towards Efficient and Effective Unlearning of Large Language Models for Recommendation | FCS | [[Link]](https://arxiv.org/abs/2403.03536) | | Generative News Recommendation | WWW 2024 | [[Link]](https://arxiv.org/abs/2403.03424) | | Bridging Language and Items for Retrieval and Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2403.03952) | | Can Small Language Models be Good Reasoners for Sequential Recommendation? | WWW 2024 | [[Link]](https://arxiv.org/abs/2403.04260) | | Aligning Large Language Models for Controllable Recommendations | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2403.05063) | | Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks | WWW 2024 | [[Link]](https://arxiv.org/abs/2403.05185) | | CFaiRLLM: Consumer Fairness Evaluation in Large-Language Model Recommender System | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2403.05668) | | CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2403.06447) | | RecAI: Leveraging Large Language Models for Next-Generation Recommender Systems | WWW 2024 | [[Link]](https://arxiv.org/abs/2403.06465) | | KELLMRec: Knowledge-Enhanced Large Language Models for Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2403.06642) | | Towards Graph Foundation Models for Personalization | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2403.07478) | | USimAgent: Large Language Models for Simulating Search Users | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2403.09142) | | The Whole is Better than the Sum: Using Aggregated Demonstrations in In-Context Learning for Sequential Recommendation | NAACL 2024 | [[Link]](https://arxiv.org/abs/2403.10135) | | PPM : A Pre-trained Plug-in Model for Click-through Rate Prediction | WWW 2024 | [[Link]](https://arxiv.org/abs/2403.10049) | | Evaluating Large Language Models as Generative User Simulators for Conversational Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2403.09738) | | Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond | ICLR 2024 | [[Link]](https://arxiv.org/abs/2403.10667) | | Harnessing Large Language Models for Text-Rich Sequential Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2403.13325) | | A Large Language Model Enhanced Sequential Recommender for Joint Video and Comment Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2403.13574) | | Could Small Language Models Serve as Recommenders? Towards Data-centric Cold-start Recommendations | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2306.17256) | | Play to Your Strengths: Collaborative Intelligence of Conventional Recommender Models and Large Language Models | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2403.16378) | | Reinforcement Learning-based Recommender Systems with Large Language Models for State Reward and Action Modeling | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2403.16948) | | Large Language Models Enhanced Collaborative Filtering | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2403.17688) | | Improving Content Recommendation: Knowledge Graph-Based Semantic Contrastive Learning for Diversity and Cold-Start Users | LREC-COLING 2024 | [[Link]](https://arxiv.org/abs/2403.18667) | | Sequential Recommendation with Latent Relations based on Large Language Model | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2403.18348) | | Enhanced Generative Recommendation via Content and Collaboration Integration | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2403.18480) | | To Recommend or Not: Recommendability Identification in Conversations with Pre-trained Language Models | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2403.18628) | | IDGenRec: LLM-RecSys Alignment with Textual ID Learning | SIGIR 2024 | [[Link]](https://arxiv.org/abs/2403.19021) | | Breaking the Length Barrier: LLM-Enhanced CTR Prediction in Long Textual User Behaviors | SIGIR 2024 | [[Link]](https://arxiv.org/abs/2403.19347) | | Make Large Language Model a Better Ranker | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2403.19181) | | Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankers | NAACL 2024 | [[Link]](https://arxiv.org/abs/2404.03192) | | IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFT | SIGIR 2024 | [[Link]](https://arxiv.org/abs/2404.02059) | | Where to Move Next: Zero-shot Generalization of LLMs for Next POI Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2404.01855) | | Tired of Plugins? Large Language Models Can Be End-To-End Recommender | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2404.00702) | | Aligning Large Language Models with Recommendation Knowledge | NAACL 2024 | [[Link]](https://arxiv.org/abs/2404.00245) | | Enhancing Content-based Recommendation via Large Language Model | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2404.00236) | | DRE: Generating Recommendation Explanations by Aligning Large Language Models at Data-level | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2404.06311) | | Optimization Methods for Personalizing Large Language Models through Retrieval Augmentation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2404.05970) | | Q-PEFT: Query-dependent Parameter Efficient Fine-tuning for Text Reranking with Large Language Models | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2404.04522) | | JobFormer: Skill-Aware Job Recommendation with Semantic-Enhanced Transformer | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2404.04313) | | PMG : Personalized Multimodal Generation with Large Language Models | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2404.08677) | | The Elephant in the Room: Rethinking the Usage of Pre-trained Language Model in Sequential Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2404.08796) | | Exact and Efficient Unlearning for Large Language Model-based Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2404.10327) | | Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender System | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2404.11343) | | Behavior Alignment: A New Perspective of Evaluating LLM-based Conversational Recommendation Systems | SIGIR 2024 | [[Link]](https://arxiv.org/abs/2404.11773) | | Generating Diverse Criteria On-the-Fly to Improve Point-wise LLM Rankers | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2404.11960) | | RecGPT: Generative Personalized Prompts for Sequential Recommendation via ChatGPT Training Paradigm | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2404.08675) | | MMGRec: Multimodal Generative Recommendation with Transformer Model | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2404.16555) | | Hi-Gen: Generative Retrieval For Large-Scale Personalized E-commerce Search | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2404.15675) | | Contrastive Quantization based Semantic Code for Generative Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2404.14774) | | ImplicitAVE: An Open-Source Dataset and Multimodal LLMs Benchmark for Implicit Attribute Value Extraction | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2404.15592) | | Large Language Models for Next Point-of-Interest Recommendation | SIGIR 2024 | [[Link]](https://arxiv.org/abs/2404.17591) | | Ranked List Truncation for Large Language Model-based Re-Ranking | SIGIR 2024 | [[Link]](https://arxiv.org/abs/2404.18185) | | Large Language Models as Conversational Movie Recommenders: A User Study | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2404.19093) | | Distillation Matters: Empowering Sequential Recommenders to Match the Performance of Large Language Model | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2405.00338) | | Efficient and Responsible Adaptation of Large Language Models for Robust Top-k Recommendations | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2405.00824) | | FairEvalLLM. A Comprehensive Framework for Benchmarking Fairness in Large Language Model Recommender Systems | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2405.02219) | | Improve Temporal Awareness of LLMs for Sequential Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2405.02778) | | CALRec: Contrastive Alignment of Generative LLMs For Sequential Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2405.02429) | | Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2405.03988) | | DynLLM: When Large Language Models Meet Dynamic Graph Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2405.07580) | | Learnable Tokenizer for LLM-based Generative Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org//2405.07314) | | CELA: Cost-Efficient Language Model Alignment for CTR Prediction | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2405.10596) | | RDRec: Rationale Distillation for LLM-based Recommendation | ACL 2024 | [[Link]](https://arxiv.org/abs/2405.10587) | | EmbSum: Leveraging the Summarization Capabilities of Large Language Models for Content-Based Recommendations | Arxiv 2024 | [[Link]](https://www.arxiv.org/abs/2405.11441) | | Reindex-Then-Adapt: Improving Large Language Models for Conversational Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2405.12119) | | RecGPT: Generative Pre-training for Text-based Recommendation | ACL 2024 | [[Link]](https://arxiv.org/abs/2405.12715) | | Let Me Do It For You: Towards LLM Empowered Recommendation via Tool Learning | SIGIR 2024 | [[Link]](https://arxiv.org/abs/2405.15114) | | Finetuning Large Language Model for Personalized Ranking | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2405.16127) | | LLMs for User Interest Exploration: A Hybrid Approach | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2405.16363) | | NoteLLM-2: Multimodal Large Representation Models for Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2405.16789) | | Multimodality Invariant Learning for Multimedia-Based New Item Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2405.15783) | | SLMRec: Empowering Small Language Models for Sequential Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2405.17890) | | Keyword-driven Retrieval-Augmented Large Language Models for Cold-start User Recommendations | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2405.19612) | | Generating Query Recommendations via LLMs | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2405.19749) | | Large Language Models Enhanced Sequential Recommendation for Long-tail User and Item | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2405.20646) | | DisCo: Towards Harmonious Disentanglement and Collaboration between Tabular and Semantic Space for Recommendation | KDD 2024 | [[Link]](https://arxiv.org/abs/2406.00011) | | LLM-RankFusion: Mitigating Intrinsic Inconsistency in LLM-based Ranking | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2406.00231) | | A Practice-Friendly Two-Stage LLM-Enhanced Paradigm in Sequential Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2406.00333) | | Large Language Models as Recommender Systems: A Study of Popularity Bias | Gen-IR@SIGIR24 | [[Link]](https://arxiv.org/abs/2406.01285) | | Privacy in LLM-based Recommendation: Recent Advances and Future Directions | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2406.01363) | | An LLM-based Recommender System Environment | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2406.01631) | | Robust Interaction-based Relevance Modeling for Online E-Commerce and LLM-based Retrieval | ECML-PKDD 2024 | [[Link]](https://arxiv.org/abs/2406.02135) | | Large Language Models Make Sample-Efficient Recommender Systems | FCS | [[Link]](https://arxiv.org/abs/2406.02368) | | XRec: Large Language Models for Explainable Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2406.02377) | | Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2406.03085) | | Large Language Models as Evaluators for Recommendation Explanations | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2406.03248) | | Text-like Encoding of Collaborative Information in Large Language Models for Recommendation | ACL 2024 | [[Link]](https://arxiv.org/abs/2406.03210) | | Item-Language Model for Conversational Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2406.02844) | | Improving LLMs for Recommendation with Out-Of-Vocabulary Tokens | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2406.08477) | | On Softmax Direct Preference Optimization for Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2406.09215) | | TokenRec: Learning to Tokenize ID for LLM-based Generative Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2406.10450) | | DELRec: Distilling Sequential Pattern to Enhance LLM-based Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2406.11156) | | TourRank: Utilizing Large Language Models for Documents Ranking with a Tournament-Inspired Strategy | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2406.11678) | | Multi-Layer Ranking with Large Language Models for News Source Recommendation | SIGIR 2024 | [[Link]](https://arxiv.org/abs/2406.11745) | | Intermediate Distillation: Data-Efficient Distillation from Black-Box LLMs for Information Retrieval | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2406.12169) | | LLM-enhanced Reranking in Recommender Systems | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2406.12433) | | LLM4MSR: An LLM-Enhanced Paradigm for Multi-Scenario Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2406.12529) | | Taxonomy-Guided Zero-Shot Recommendations with LLMs | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2406.14043) | | EAGER: Two-Stream Generative Recommender with Behavior-Semantic Collaboration | KDD 2024 | [[Link]](https://arxiv.org/abs/2406.14017) | | An Investigation of Prompt Variations for Zero-shot LLM-based Rankers | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2406.14117) | | Optimizing Novelty of Top-k Recommendations using Large Language Models and Reinforcement Learning | KDD 2024 | [[Link]](https://arxiv.org/abs/2406.14169) | | Enhancing Collaborative Semantics of Language Model-Driven Recommendations via Graph-Aware Learning | https://arxiv.org/abs/2406.13235 | [[Link]](https://arxiv.org/abs/2406.13235) | | Decoding Matters: Addressing Amplification Bias and Homogeneity Issue for LLM-based Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2406.14900) | | FIRST: Faster Improved Listwise Reranking with Single Token Decoding | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2406.15657) | | LLM-Powered Explanations: Unraveling Recommendations Through Subgraph Reasoning | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2406.15859) | | DemoRank: Selecting Effective Demonstrations for Large Language Models in Ranking Task | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2406.16332) | | ELCoRec: Enhance Language Understanding with Co-Propagation of Numerical and Categorical Features for Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2406.18825) | | Generative Explore-Exploit: Training-free Optimization of Generative Recommender Systems using LLM Optimizers | ACL 2024 | [[Link]](https://arxiv.org/abs/2406.05255) | | ProductAgent: Benchmarking Conversational Product Search Agent with Asking Clarification Questions | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2407.00942) | | MemoCRS: Memory-enhanced Sequential Conversational Recommender Systems with Large Language Models | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2407.04960) | | Preference Distillation for Personalized Generative Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2407.05033) | | Towards Bridging the Cross-modal Semantic Gap for Multi-modal Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2407.05420) | | Language Models Encode Collaborative Signals in Recommendation | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2407.05441) | | A Neural Matrix Decomposition Recommender System Model based on the Multimodal Large Language Model | Arxiv 2024 | [[Link]](https://arxiv.org/abs/2407.08942) | | LLMGR: Large Language Model-based Generative Retrieval in Alipay Search | SIGIR 2024 | [[Link]](https://dl.acm.org/doi/abs/10.1145/3626772.3661364) | | Enhancing Sequential Recommenders with Augmented Knowledge from Aligned Large Language Models | SIGIR 2024 | [[Link]](https://dl.acm.org/doi/10.1145/3626772.3657782) |