359 citations · 2k across the 127 of their papers we have counts for
6 papers · 2 filters
Disentangled Modeling of Domain and Relevance for Adaptable Dense Retrieval
Jingtao Zhan, Qingyao Ai, Yiqun Liu +4
Recent advance in Dense Retrieval (DR) techniques has significantly improved the effectiveness of first-stage retrieval. Trained with large-scale supervised data, DR models can enc…
Towards Representation Alignment and Uniformity in Collaborative Filtering
Chenyang Wang, Yuanqing Yu, Weizhi Ma +4
Collaborative filtering (CF) plays a critical role in the development of recommender systems. Most CF methods utilize an encoder to embed users and items into the same representati…
A Survey on the Fairness of Recommender Systems
Yifan Wang, Weizhi Ma, Min Zhang +2
Recommender systems are an essential tool to relieve the information overload challenge and play an important role in people's daily lives. Since recommendations involve allocation…
Evaluating Interpolation and Extrapolation Performance of Neural Retrieval Models
Jingtao Zhan, Xiaohui Xie, Jiaxin Mao +4
A retrieval model should not only interpolate the training data but also extrapolate well to the queries that are different from the training data. While neural retrieval models ha…
Towards Better Understanding of User Satisfaction in Open-Domain Conversational Search
Zhumin Chu, Qingyao Ai, Zhihong Wang +5
With the increasing popularity of conversational search, how to evaluate the performance of conversational search systems has become an important question in the IR community. Exis…
Recommendation Unlearning
Chong Chen, Fei Sun, Min Zhang +1
Recommender systems provide essential web services by learning users' personal preferences from collected data. However, in many cases, systems also need to forget some training da…