3 citations · 3 across the 5 of their papers we have counts for
5 papers
CESRec: Constructing Pseudo Interactions for Sequential Recommendation via Conversational Feedback
Yifan Wang, Shen Gao, Jiabao Fang +3
Sequential Recommendation Systems (SRS) have become essential in many real-world applications. However, existing SRS methods often rely on collaborative filtering signals and fail…
Evolution without Large Models: Training Language Model with Task Principles
Minghang Zhu, Shen Gao, Zhengliang Shi +5
A common training approach for language models involves using a large-scale language model to expand a human-provided dataset, which is subsequently used for model training.This me…
DRE: Generating Recommendation Explanations by Aligning Large Language Models at Data-level
Shen Gao, Yifan Wang, Jiabao Fang +3
Recommendation systems play a crucial role in various domains, suggesting items based on user behavior.However, the lack of transparency in presenting recommendations can lead to u…
Generative News Recommendation
Shen Gao, Jiabao Fang, Quan Tu +4
Most existing news recommendation methods tackle this task by conducting semantic matching between candidate news and user representation produced by historical clicked news. Howev…
A Multi-Agent Conversational Recommender System
Jiabao Fang, Shen Gao, Pengjie Ren +3
Due to strong capabilities in conducting fluent, multi-turn conversations with users, Large Language Models (LLMs) have the potential to further improve the performance of Conversa…