24 citations · 39 across the 4 of their papers we have counts for
4 papers · 1 filter
Aligning Explanations for Recommendation with Rating and Feature via Maximizing Mutual Information
Yurou Zhao, Yiding Sun, Ruidong Han +6
Providing natural language-based explanations to justify recommendations helps to improve users' satisfaction and gain users' trust. However, as current explanation generation meth…
RNE: A Scalable Network Embedding for Billion-scale Recommendation
Jianbin Lin, Daixin Wang, Lu Guan +5
Nowadays designing a real recommendation system has been a critical problem for both academic and industry. However, due to the huge number of users and items, the diversity and dy…
Recent Advances in Diversified Recommendation
Qiong Wu, Yong Liu, Chunyan Miao +3
With the rapid development of recommender systems, accuracy is no longer the only golden criterion for evaluating whether the recommendation results are satisfying or not. In recen…
Diversity-Promoting Deep Reinforcement Learning for Interactive Recommendation
Yong Liu, Yinan Zhang, Qiong Wu +5
Interactive recommendation that models the explicit interactions between users and the recommender system has attracted a lot of research attentions in recent years. Most previous…