25 citations · 33 across the 7 of their papers we have counts for
5 papers · 1 filter
Unlocking the Hidden Treasures: Enhancing Recommendations with Unlabeled Data
Yuhan Zhao, Rui Chen, Qilong Han +2
Collaborative filtering (CF) stands as a cornerstone in recommender systems, yet effectively leveraging the massive unlabeled data presents a significant challenge. Current researc…
From Pairwise to Ranking: Climbing the Ladder to Ideal Collaborative Filtering with Pseudo-Ranking
Yuhan Zhao, Rui Chen, Li Chen +3
Intuitively, an ideal collaborative filtering (CF) model should learn from users' full rankings over all items to make optimal top-K recommendations. Due to the absence of such ful…
Adaptive Hardness Negative Sampling for Collaborative Filtering
Riwei Lai, Rui Chen, Qilong Han +2
Negative sampling is essential for implicit collaborative filtering to provide proper negative training signals so as to achieve desirable performance. We experimentally unveil a c…
Augmented Negative Sampling for Collaborative Filtering
Yuhan Zhao, Rui Chen, Riwei Lai +3
Negative sampling is essential for implicit-feedback-based collaborative filtering, which is used to constitute negative signals from massive unlabeled data to guide supervised lea…
Multi-Output Recommender: Items, Groups and Friends, and Their Mutual Contributing Effects
Wei Zeng, Li Chen
Due to the development of social media technology, it becomes easier for users to gather together to form groups. Take the Last.fm for example, users can join groups they may be in…