The main goal of reading paper is not just understanding it. Try to understand the key concept, but we need to get new ideas and research directions from the paper.
Paper information
title : Multi-Graph Group Collaborative Filtering
authors : Jiang, Bo
venue :International Conference on Multimedia Retrieval(ICMR) 2020 (http://icmr2020.org/)
The task of recommending an item or an event to a user group attracts wide attention. Most existing works obtain group preference by aggregating personalized preferences in the same group. However, the groups, users, and items are connected in a more complex structure, e.g.the users in the same group may have different preferences. Thus, it is important to introduce correlations among groups, users, and items into embedding learning. To address this problem, we propose Multi-Graph Group Collaborative Filtering (MGGCF), which refines the group, user and item representations according to three bipartite graphs. Moreover, since MGGCF refines the group, user and item embeddings simultaneously, it would benefit both the group recommendation tasks and the individual recommendation tasks. Extensive experiments are conducted on one real-world dataset and two synthetic datasets. Empirical results demonstrate that MGGCF significantly improves not only the group recommendation but also the item recommendation. Further analysis verifies the importance of embedding propagation for learning better user, group, item representations, which reveals the rationality and effectiveness of MGGCF.
Summary: problems to address, key ideas, quick results
The main goal of reading paper is not just understanding it. Try to understand the key concept, but we need to get new ideas and research directions from the paper.
Paper information
title : Multi-Graph Group Collaborative Filtering
authors : Jiang, Bo
venue :International Conference on Multimedia Retrieval(ICMR) 2020 (http://icmr2020.org/)
pdf link : https://paperpile.com/shared/5cwWn6
github link : X
Abstract
The task of recommending an item or an event to a user group attracts wide attention. Most existing works obtain group preference by aggregating personalized preferences in the same group. However, the groups, users, and items are connected in a more complex structure, e.g.the users in the same group may have different preferences. Thus, it is important to introduce correlations among groups, users, and items into embedding learning. To address this problem, we propose Multi-Graph Group Collaborative Filtering (MGGCF), which refines the group, user and item representations according to three bipartite graphs. Moreover, since MGGCF refines the group, user and item embeddings simultaneously, it would benefit both the group recommendation tasks and the individual recommendation tasks. Extensive experiments are conducted on one real-world dataset and two synthetic datasets. Empirical results demonstrate that MGGCF significantly improves not only the group recommendation but also the item recommendation. Further analysis verifies the importance of embedding propagation for learning better user, group, item representations, which reveals the rationality and effectiveness of MGGCF.
Summary: problems to address, key ideas, quick results
https://docs.google.com/document/d/1_6ni99Ry6YX4o67HH52jHwClhHpSLjp7Nccs_hD1bec/edit?usp=sharing
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