1.2k citations · 3.9k across the 22 of their papers we have counts for
7 papers · 1 filter
Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation
Zihao Zhao, Jiawei Chen, Sheng Zhou +4
Recommender system usually suffers from severe popularity bias -- the collected interaction data usually exhibits quite imbalanced or even long-tailed distribution over items. Such…
Improving Document Representations by Generating Pseudo Query Embeddings for Dense Retrieval
Hongyin Tang, Xingwu Sun, Beihong Jin +3
Recently, the retrieval models based on dense representations have been gradually applied in the first stage of the document retrieval tasks, showing better performance than tradit…
Leveraging Historical Interaction Data for Improving Conversational Recommender System
Kun Zhou, Wayne Xin Zhao, Hui Wang +4
Recently, conversational recommender system (CRS) has become an emerging and practical research topic. Most of the existing CRS methods focus on learning effective preference repre…
S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization
Kun Zhou, Hui Wang, Wayne Xin Zhao +5
Recently, significant progress has been made in sequential recommendation with deep learning. Existing neural sequential recommendation models usually rely on the item prediction l…
Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation
Hongwei Wang, Fuzheng Zhang, Miao Zhao +3
Collaborative filtering often suffers from sparsity and cold start problems in real recommendation scenarios, therefore, researchers and engineers usually use side information to a…
Cross-domain novelty seeking trait mining for sequential recommendation
Fuzhen Zhuang, Yingmin Zhou, Fuzheng Zhang +3
Transfer learning has attracted a large amount of interest and research in last decades, and some efforts have been made to build more precise recommendation systems. Most previous…
RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
Hongwei Wang, Fuzheng Zhang, Jialin Wang +4
To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve…