Open editorialbot opened 2 months ago
editorialbot
commented
2 months ago Hello humans, I'm @editorialbot, a robot that can help you with some common editorial tasks.
For a list of things I can do to help you, just type:
@editorialbot commandsFor example, to regenerate the paper pdf after making changes in the paper source files, type:
@editorialbot generate pdfSoftware report:
github.com/AlDanial/cloc v 1.90 T=0.05 s (1757.8 files/s, 154268.9 lines/s)
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Language files blank comment code
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TeX 14 399 178 2938
Julia 55 645 619 2306
Markdown 12 144 0 303
YAML 3 0 0 96
Ruby 1 8 4 45
TOML 2 5 0 23
make 1 3 0 7
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SUM: 88 1204 801 5718
-------------------------------------------------------------------------------Commit count by author:
330 Takuya Kitazawa
2 Hieronimo
1 Dhairya Gandhi
1 Julia TagBot
1 Tony KelmanPaper file info:
📄 Wordcount for paper.tex is 146
🔴 Failed to discover a Statement of need section in paper
editorialbot
commented
2 months ago License info:
✅ License found: MIT License (Valid open source OSI approved license)
editorialbot
commented
2 months ago Reference check summary (note 'MISSING' DOIs are suggestions that need verification):
OK DOIs
- None
MISSING DOIs
- 10.1137/141000671 may be a valid DOI for title: Julia: A Fresh Approach to Numerical Computing
- 10.1145/3340531.3412778 may be a valid DOI for title: LensKit for Python: Next-Generation Software for ...
- No DOI given, and none found for title: MyMediaLite: A Free Recommender System Library
- No DOI given, and none found for title: LibRec: A Java Library for Recommender Systems
- No DOI given, and none found for title: The MovieLens Datasets: History and Context
- 10.18653/v1/d19-1018 may be a valid DOI for title: Justifying Recommendations using Distantly-Labele...
- No DOI given, and none found for title: 2nd Workshop on Information Heterogeneity and Fus...
- 10.1145/138859.138867 may be a valid DOI for title: Using Collaborative Filtering to Weave an Informa...
- No DOI given, and none found for title: Item-Based Collaborative Filtering Recommendation...
- 10.1145/3130348.3130372 may be a valid DOI for title: An Algorithmic Framework for Performing Collabora...
- No DOI given, and none found for title: Item-Based Top-N Recommendation Algorithms
- No DOI given, and none found for title: Application of Dimensionality Reduction in Recomm...
- No DOI given, and none found for title: Matrix Factorization Techniques for Recommender S...
- No DOI given, and none found for title: Netflix Update: Try This at Home
- No DOI given, and none found for title: Feature-Based Matrix Factorization
- No DOI given, and none found for title: BPR: Bayesian Personalized Ranking from Implicit ...
- No DOI given, and none found for title: Multiverse Recommendation: N-Dimensional Tensor F...
- 10.1007/978-0-387-85820-3_3 may be a valid DOI for title: Content-Based Recommender Systems: State of the A...
- 10.1145/2792838.2796542 may be a valid DOI for title: Factorization Machines for Hybrid Recommendation ...
- 10.1145/2168752.2168771 may be a valid DOI for title: Factorization Machines with libFM
- 10.1145/2124295.2124313 may be a valid DOI for title: Learning Recommender Systems with Adaptive Regula...
- No DOI given, and none found for title: Social Network and Click-Through Prediction with ...
- No DOI given, and none found for title: RecPack: An (Other) Experimentation Toolkit for T...
- 10.1007/978-0-387-85820-3_8 may be a valid DOI for title: Evaluating Recommendation Systems
- No DOI given, and none found for title: A Survey of Serendipity in Recommender Systems
- 10.1145/1060745.1060754 may be a valid DOI for title: Improving Recommendation Lists through Topic Dive...
- No DOI given, and none found for title: Julia 1.0 Programming Complete Reference Guide: D...
- No DOI given, and none found for title: Julia Programming Projects: Learn Julia 1.x by Bu...
- No DOI given, and none found for title: OFF-Set: One-Pass Factorization of Feature Sets f...
- No DOI given, and none found for title: The Netflix Prize
- No DOI given, and none found for title: Introduction to Information Retrieval
- 10.2139/ssrn.3378581 may be a valid DOI for title: Recommender Systems and Their Ethical Challenges
INVALID DOIs
- None
:point_right::page_facing_up: Download article proof :page_facing_up: View article proof on GitHub :page_facing_up: :point_left:
lucaferranti
commented
2 months ago @PGimenez, @abhijithch, thank you very much for volunteering as reviewers!
I will be the editor for this submission, feel free to ping me to ask any questions you may have.
You can find review guidelines here feel free to ask at any point if something is unclear.
As a first step, you can generate your checklist by running
@editorialbot generate my checklistYou can write your review comments here or directly open an issue in the paper repository
lucaferranti
commented
2 months ago @editorialbot remove @lucaferranti as reviewer
editorialbot
commented
2 months ago @lucaferranti removed from the reviewers list!
lucaferranti
commented
1 month ago Hi @abhijithch and @PGimenez :wave: ,
just checking in to see if you had time to start the review. Any timeline estimate?
PGimenez
commented
1 month ago paper.tex file include a list of authors with their affiliations?PGimenez
commented
1 month ago Hi, I'd like to submit my review of the paper "Recommendation.jl: A Framework for Building Recommender Systems in Julia" below.
I believe this is a good paper on recommender systems. It does a deep enough review on the most popular methods used in the literature, and provides implementation details in the package. The paper is well-written, with clear language and good usage of math notation to explain concepts. I think the pape is apt for publication with minor changes/corrections.
There are a few minor points I'd like to raise, which can also be found in the attached annotated PDF:
It seems Figure 3 illustrates the fit and recommendation operations, not the data conversion into a DataAccessor
is it possible to create a DataAccessor from a matrix R? R could indeed be converted into an event array by having the users and items be the row/column indices of the matrix, and the ratings be the entries of the matrix
In 3.2.2 you mention that handling big matrices is very expensive. However, SVD is also known for being extremely costly ( O(N^3)) when used in matrix completion, as you mention in the next section 3.2.3. Therefore, SVD does not solve the resource cost problem.
It is not clear to me how FMs relate to the (user,item) model used earlier, although I'll admit I hadn't seen FMs before. I assume x would be a feature vector for a user, and y(x) would yield a rating for this vector. But, how is x related to the item being rated?
Just before 4.2.1, Sentence seems incomplete, I'd reorder them like:
let u be a target user, I the set of all items, and I_u the set of. truth items
Or leave as it is and add "be" before each variable like
let a target user be u, the set of all items be I...
Then, a few more points:
The cold-start problem
In 3.4 you state that the cold-start problem arises when "there is not enough historical data to capture meaningful information". The question then is, how much data is needed? I believe a single rating is enough.
To me, the issue here is adding new users or items to the system since these haven't rated or haven't been rated by anyone yet. Therefore the system cannot find items similar to the one the user likes (which is none since they've rated nothing)
From a formulation standpoint, new users and items have an empty row/column in matrix R. When factorizing R with any of the earlier methods, the coefficients associated with the new user/item (p_i, q_j for instance) will be all-zero. This would yield no recommendations.
This can be solved by leveraging additional information in the form of feature vectors (the user attribute parameter) for either users or items. In fact, I believe this is what the content-based filtering in 3.4 is actually doing since it requires the user preferences (but no prior ratings).
This usage of attribute vectors can be incorporated into the matrix factorization technique as well, as shown in these papers
Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering Approach Matrix completion and extrapolation via kernel regression (section 3. Disclosure, I wrote this one 😄)
My point here is: If most of the implemented algorithms can't solve the cold-start problem, how usable are they in real-world situations?
Experimental results
The results are good for showcasing the different implemented metrics and having a better understanding of their meaning. However, I find it lacking that only the SVD method is used. I'd expect the experiments to also show how this package allows to easily switch between algorithms. Also, some benchmarks as to their computational cost.
Package documentation
The package documentation is good, as it covers the algorithms in detail. It'd be good to also have one or two tutorials like the script included in the paper. (I there are already, sorry, I couldn't find any)
lucaferranti
commented
1 month ago Thank you very much @PGimenez for the review!
@takuti please take a look at the comments and let us know when you have addressed the review comments
PGimenez
commented
1 month ago sorry, forgot to attach the annotated PDf =) 10.21105.jcon.00147.pdf
takuti
commented
1 month ago Thank you for the review comments, @PGimenez! Just informing you that I am actively working on the points and hopefully update you in the coming weeks. cc: @lucaferranti
lucaferranti
commented
2 weeks ago Hi @abhijithch :wave: ,
how is it going with the review? Do you happen to have an estimated timeline?
Submitting author: !--author-handle-->@takuti<!--end-author-handle-- (Takuya Kitazawa) Repository: https://github.com/takuti/Recommendation.jl Branch with paper.md (empty if default branch): Version: Editor: !--editor-->@lucaferranti<!--end-editor-- Reviewers: @abhijithch, @pgimenez Archive: Pending
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