32 citations · 53 across the 5 of their papers we have counts for
6 papers
A Bird's-eye View of Reranking: from List Level to Page Level
Yunjia Xi, Jianghao Lin, Weiwen Liu +5
Reranking, as the final stage of multi-stage recommender systems, refines the initial lists to maximize the total utility. With the development of multimedia and user interface des…
Multi-Level Interaction Reranking with User Behavior History
Yunjia Xi, Weiwen Liu, Jieming Zhu +6
As the final stage of the multi-stage recommender system (MRS), reranking directly affects users' experience and satisfaction, thus playing a critical role in MRS. Despite the impr…
An Adversarial Imitation Click Model for Information Retrieval
Xinyi Dai, Jianghao Lin, Weinan Zhang +7
Modern information retrieval systems, including web search, ads placement, and recommender systems, typically rely on learning from user feedback. Click models, which study how use…
U-rank: Utility-oriented Learning to Rank with Implicit Feedback
Xinyi Dai, Jiawei Hou, Qing Liu +6
Learning to rank with implicit feedback is one of the most important tasks in many real-world information systems where the objective is some specific utility, e.g., clicks and rev…
Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning
Sijin Zhou, Xinyi Dai, Haokun Chen +5
Interactive recommender system (IRS) has drawn huge attention because of its flexible recommendation strategy and the consideration of optimal long-term user experiences. To deal w…
Large-scale Interactive Recommendation with Tree-structured Policy Gradient
Haokun Chen, Xinyi Dai, Han Cai +5
Reinforcement learning (RL) has recently been introduced to interactive recommender systems (IRS) because of its nature of learning from dynamic interactions and planning for long-…