Publications (5)
Deep Situation-Aware Interaction Network for Click-Through Rate Prediction
Yimin Lv, Shuli Wang, Beihong Jin +6
User behavior sequence modeling plays a significant role in Click-Through Rate (CTR) prediction on e-commerce platforms. Except for the interacted items, user behaviors contain ric…
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 Deep Behavior Path Matching Network for Click-Through Rate Prediction
Jian Dong, Yisong Yu, Yapeng Zhang +6
User behaviors on an e-commerce app not only contain different kinds of feedback on items but also sometimes imply the cognitive clue of the user's decision-making. For understandi…
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…