6 citations · 13 across the 7 of their papers we have counts for
7 papers
Action-Aware Generative Sequence Modeling for Short Video Recommendation
Wenhao Li, Zihan Lin, Zhengxiao Guo +7
With the rapid development of the Internet, users have increasingly higher expectations for the recommendation accuracy of online content consumption platforms. However, short vide…
UMRE: A Unified Monotonic Transformation for Ranking Ensemble in Recommender Systems
Zhengrui Xu, Zhe Yang, Zhengxiao Guo +5
Industrial recommender systems commonly rely on ensemble sorting (ES) to combine predictions from multiple behavioral objectives. Traditionally, this process depends on manually de…
Comment Staytime Prediction with LLM-enhanced Comment Understanding
Changshuo Zhang, Zihan Lin, Shukai Liu +2
In modern online streaming platforms, the comments section plays a critical role in enhancing the overall user experience. Understanding user behavior within the comments section i…
Boosting Feedback Efficiency of Interactive Reinforcement Learning by Adaptive Learning from Scores
Shukai Liu, Chenming Wu, Ying Li +1
Interactive reinforcement learning has shown promise in learning complex robotic tasks. However, the process can be human-intensive due to the requirement of a large amount of inte…
Contrastive Cross-domain Recommendation in Matching
Ruobing Xie, Qi Liu, Liangdong Wang +3
Cross-domain recommendation (CDR) aims to provide better recommendation results in the target domain with the help of the source domain, which is widely used and explored in real-w…
Improving Accuracy and Diversity in Matching of Recommendation with Diversified Preference Network
Ruobing Xie, Qi Liu, Shukai Liu +4
Recently, real-world recommendation systems need to deal with millions of candidates. It is extremely challenging to conduct sophisticated end-to-end algorithms on the entire corpu…
Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction
Wen Wang, Wei Zhang, Shukai Liu +4
Session-based target behavior prediction aims to predict the next item to be interacted with specific behavior types (e.g., clicking). Although existing methods for session-based b…