activity
20162023
most citedCLEAR: Contrastive Learning for Sentence Representation

229 citations · 694 across the 18 of their papers we have counts for

collaborators
Showing cs.IRShow all

15 papers · 1 filter

cs.IR20211 cited

Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning

Yang Deng, Yaliang Li, Fei Sun +2

Conversational recommender systems (CRS) enable the traditional recommender systems to explicitly acquire user preferences towards items and attributes through interactive conversa…

cs.IR20212 cited

Explore User Neighborhood for Real-time E-commerce Recommendation

Xu Xie, Fei Sun, Xiaoyong Yang +4

Recommender systems play a vital role in modern online services, such as Amazon and Taobao. Traditional personalized methods, which focus on user-item (UI) relations, have been wid…

cs.IR202114 cited

Variation Control and Evaluation for Generative SlateRecommendations

Shuchang Liu, Fei Sun, Yingqiang Ge +2

Slate recommendation generates a list of items as a whole instead of ranking each item individually, so as to better model the intra-list positional biases and item relations. In o…

cs.IR2021197 cited

Towards Long-term Fairness in Recommendation

Yingqiang Ge, Shuchang Liu, Ruoyuan Gao +8

As Recommender Systems (RS) influence more and more people in their daily life, the issue of fairness in recommendation is becoming more and more important. Most of the prior appro…

cs.IR2020

Contrastive Learning for Sequential Recommendation

Xu Xie, Fei Sun, Zhaoyang Liu +4

Sequential recommendation methods play a crucial role in modern recommender systems because of their ability to capture a user's dynamic interest from her/his historical interactio…

cs.IR202019 cited

Learning Personalized Risk Preferences for Recommendation

Yingqiang Ge, Shuyuan Xu, Shuchang Liu +3

The rapid growth of e-commerce has made people accustomed to shopping online. Before making purchases on e-commerce websites, most consumers tend to rely on rating scores and revie…