119 citations · 393 across the 22 of their papers we have counts for
5 papers · 1 filter
Practitioners Versus Users: A Value-Sensitive Evaluation of Current Industrial Recommender System Design
Zhilong Chen, Jinghua Piao, Xiaochong Lan +4
Recommender systems are playing an increasingly important role in alleviating information overload and supporting users' various needs, e.g., consumption, socialization, and entert…
DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity Prediction
Yinfeng Li, Chen Gao, Quanming Yao +3
Spatiotemporal activity prediction, aiming to predict user activities at a specific location and time, is crucial for applications like urban planning and mobile advertising. Exist…
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…
Causal Inference in Recommender Systems: A Survey and Future Directions
Chen Gao, Yu Zheng, Wenjie Wang +3
Recommender systems have become crucial in information filtering nowadays. Existing recommender systems extract user preferences based on the correlation in data, such as behaviora…
Disentangling Long and Short-Term Interests for Recommendation
Yu Zheng, Chen Gao, Jianxin Chang +4
Modeling user's long-term and short-term interests is crucial for accurate recommendation. However, since there is no manually annotated label for user interests, existing approach…