most citedTreatment Effect Estimation for User Interest Exploration on Recommender Systems

8 citations · 9 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV2025

VINCIE: Unlocking In-context Image Editing from Video

Leigang Qu, Feng Cheng, Ziyan Yang +7

In-context image editing aims to modify images based on a contextual sequence comprising text and previously generated images. Existing methods typically depend on task-specific pi…

cs.AI2025

Reinforced Latent Reasoning for LLM-based Recommendation

Yang Zhang, Wenxin Xu, Xiaoyan Zhao +4

Large Language Models (LLMs) have demonstrated impressive reasoning capabilities in complex problem-solving tasks, sparking growing interest in their application to preference reas…

cs.CL2025

MTR-Bench: A Comprehensive Benchmark for Multi-Turn Reasoning Evaluation

Xiaoyuan Li, Keqin Bao, Yubo Ma +6

Recent advances in Large Language Models (LLMs) have shown promising results in complex reasoning tasks. However, current evaluations predominantly focus on single-turn reasoning s…

cs.IR2024

Proactive Recommendation in Social Networks: Steering User Interest with Causal Inference

Hang Pan, Shuxian Bi, Wenjie Wang +3

Recommending items that solely cater to users' historical interests narrows users' horizons. Recent works have considered steering target users beyond their historical interests by…

cs.IR2024★ 1 cited

Leveraging LLMs for Influence Path Planning in Proactive Recommendation

Mingze Wang, Shuxian Bi, Wenjie Wang +3

Recommender systems are pivotal in Internet social platforms, yet they often cater to users' historical interests, leading to critical issues like echo chambers. To broaden user ho…

cs.IR2024★ 8 cited

Treatment Effect Estimation for User Interest Exploration on Recommender Systems

Jiaju Chen, Wenjie Wang, Chongming Gao +3

Recommender systems learn personalized user preferences from user feedback like clicks. However, user feedback is usually biased towards partially observed interests, leaving many…