132 citations · 332 across the 15 of their papers we have counts for
11 papers · 1 filter
Disentangled Interest Network for Out-of-Distribution CTR Prediction
Yu Zheng, Chen Gao, Jianxin Chang +5
Click-through rate (CTR) prediction, which estimates the probability of a user clicking on a given item, is a critical task for online information services. Existing approaches oft…
Inverse Learning with Extremely Sparse Feedback for Recommendation
Guanyu Lin, Chen Gao, Yu Zheng +8
Modern personalized recommendation services often rely on user feedback, either explicit or implicit, to improve the quality of services. Explicit feedback refers to behaviors like…
Mixed Attention Network for Cross-domain Sequential Recommendation
Guanyu Lin, Chen Gao, Yu Zheng +8
In modern recommender systems, sequential recommendation leverages chronological user behaviors to make effective next-item suggestions, which suffers from data sparsity issues, es…
Dual-interest Factorization-heads Attention for Sequential Recommendation
Guanyu Lin, Chen Gao, Yu Zheng +6
Accurate user interest modeling is vital for recommendation scenarios. One of the effective solutions is the sequential recommendation that relies on click behaviors, but this is n…
Mutual Harmony: Sequential Recommendation with Dual Contrastive Network
Guanyu Lin, Chen Gao, Yinfeng Li +6
With the outbreak of today's streaming data, the sequential recommendation is a promising solution to achieve time-aware personalized modeling. It aims to infer the next interacted…
DVR: Micro-Video Recommendation Optimizing Watch-Time-Gain under Duration Bias
Yu Zheng, Chen Gao, Jingtao Ding +4
Recommender systems are prone to be misled by biases in the data. Models trained with biased data fail to capture the real interests of users, thus it is critical to alleviate the…