9 citations · 10 across the 9 of their papers we have counts for
8 papers · 1 filter
Aligning Performance with Contribution: Towards Contribution-Aware Fair Recommendation
Shuai Zhang, Hui Fang, Zun Sun
Existing research on user fairness in recommender systems has developed diverse objectives. However, it has paid limited attention to a distinct distributive perspective: whether u…
Context-Aware Disentanglement for Cross-Domain Sequential Recommendation: A Causal View
Xingzi Wang, Qingtian Bian, Hui Fang
Cross-Domain Sequential Recommendation (CDSR) aims to en-hance recommendation quality by transferring knowledge across domains, offering effective solutions to data sparsity and co…
ReGeS: Reciprocal Retrieval-Generation Synergy for Conversational Recommender Systems
Dayu Yang, Hui Fang
Connecting conversation with external domain knowledge is vital for conversational recommender systems (CRS) to correctly understand user preferences. However, existing solutions e…
Research on Conversational Recommender System Considering Consumer Types
Yaying Luo, Hui Fang, Zhu Sun
Conversational Recommender Systems (CRS) provide personalized services through multi-turn interactions, yet most existing methods overlook users' heterogeneous decision-making styl…
PreQRAG -- Classify and Rewrite for Enhanced RAG
Damian Martinez, Catalina Riano, Hui Fang
This paper presents the submission of the UDInfo team to the SIGIR 2025 LiveRAG Challenge. We introduce PreQRAG, a Retrieval Augmented Generation (RAG) architecture designed to imp…
Towards Fair and Rigorous Evaluations: Hyperparameter Optimization for Top-N Recommendation Task with Implicit Feedback
Hui Fang, Xu Feng, Lu Qin +1
The widespread use of the internet has led to an overwhelming amount of data, which has resulted in the problem of information overload. Recommender systems have emerged as a solut…
A Simple Yet Effective Approach for Diversified Session-Based Recommendation
Qing Yin, Hui Fang, Zhu Sun +1
Session-based recommender systems (SBRSs) have become extremely popular in view of the core capability of capturing short-term and dynamic user preferences. However, most SBRSs pri…
Adaptive In-Context Learning with Large Language Models for Bundle Generation
Zhu Sun, Kaidong Feng, Jie Yang +4
Most existing bundle generation approaches fall short in generating fixed-size bundles. Furthermore, they often neglect the underlying user intents reflected by the bundles in the…