4 citations · 4 across the 10 of their papers we have counts for
10 papers
Collaborative Interest-aware Graph Learning for Group Identification
Rui Zhao, Beihong Jin, Beibei Li +1
With the popularity of social media, an increasing number of users are joining group activities on online social platforms. This elicits the requirement of group identification (GI…
Semantic Gaussian Mixture Variational Autoencoder for Sequential Recommendation
Beibei Li, Tao Xiang, Beihong Jin +2
Variational AutoEncoder (VAE) for Sequential Recommendation (SR), which learns a continuous distribution for each user-item interaction sequence rather than a determinate embedding…
AsyCo: An Asymmetric Dual-task Co-training Model for Partial-label Learning
Beibei Li, Yiyuan Zheng, Beihong Jin +3
Partial-Label Learning (PLL) is a typical problem of weakly supervised learning, where each training instance is annotated with a set of candidate labels. Self-training PLL models…
Denoising Long- and Short-term Interests for Sequential Recommendation
Xinyu Zhang, Beibei Li, Beihong Jin
User interests can be viewed over different time scales, mainly including stable long-term preferences and changing short-term intentions, and their combination facilitates the com…
Orthogonal Hyper-category Guided Multi-interest Elicitation for Micro-video Matching
Beibei Li, Beihong Jin, Yisong Yu +4
Watching micro-videos is becoming a part of public daily life. Usually, user watching behaviors are thought to be rooted in their multiple different interests. In the paper, we pro…
A Vlogger-augmented Graph Neural Network Model for Micro-video Recommendation
Weijiang Lai, Beihong Jin, Beibei Li +2
Existing micro-video recommendation models exploit the interactions between users and micro-videos and/or multi-modal information of micro-videos to predict the next micro-video a…
Improving Micro-video Recommendation by Controlling Position Bias
Yisong Yu, Beihong Jin, Jiageng Song +3
As the micro-video apps become popular, the numbers of micro-videos and users increase rapidly, which highlights the importance of micro-video recommendation. Although the micro-vi…
Improving Micro-video Recommendation via Contrastive Multiple Interests
Beibei Li, Beihong Jin, Jiageng Song +3
With the rapid increase of micro-video creators and viewers, how to make personalized recommendations from a large number of candidates to viewers begins to attract more and more a…
A Behavior-aware Graph Convolution Network Model for Video Recommendation
Wei Zhuo, Kunchi Liu, Taofeng Xue +7
Interactions between users and videos are the major data source of performing video recommendation. Despite lots of existing recommendation methods, user behaviors on videos, which…
Improving Sequential Recommendation with Attribute-augmented Graph Neural Networks
Xinzhou Dong, Beihong Jin, Wei Zhuo +2
Many practical recommender systems provide item recommendation for different users only via mining user-item interactions but totally ignoring the rich attribute information of ite…