Paper-Conference

Enhancing New-item Fairness in Dynamic Recommender Systems
“New-items play a crucial role in recommender systems (RSs) for delivering fresh and engaging user experiences. However, …
Huizhong Guo, Zhu Sun, Dongxia Wang, Tianjun Wei, Jinfeng Li, Jie Zhang
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LightKG: Efficient Knowledge-Aware Recommendations with Simplified GNN Architecture
‘Recently, Graph Neural Networks (GNNs) have become the dominant approach for Knowledge Graph-aware Recommender Systems (KGRSs) …
Yanhui Li, Dongxia Wang, Zhu Sun, Haonan Zhang, Huizhong Guo
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LLM4RSR: Large Language Models as Data Correctors for Robust Sequential Recommendation
Sequential Recommenders (SRs) are trained to predict the next item as the target given its preceding items as the input, assuming every …
Yatong Sun, Xiaochun Yang, Zhu Sun, Yan Wang, Bin Wang, Xinghua Qu
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Model-Agnostic Social Network Refinement with Diffusion Models for Robust Social Recommendation
“Social recommendations (SRs) aim to enhance preference modeling by integrating social networks. However, their effectiveness is …
Youchen Sun, Zhu Sun, Yingpeng Du, Jie Zhang, Yew-Soon Ong
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Re2LLM: Reflective Reinforcement Large Language Model for Session-based Recommendation
Emerging advancements in large language models (LLMs) show significant potential for enhancing recommendations. However, prompt-based …
Ziyan Wang, Yingpeng Du, Zhu Sun, Haoyan Chua, Kaidong Feng, Wenya Wang, Jie Zhang
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Adaptive In-Context Learning with Large Language Models for Bundle Generation
Zhu Sun, Kaidong Feng, Jie Yang, Xinghua Qu, Hui Fang, Yew-Soon Ong, Wenyuan Liu
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An Empirical Analysis on Multi-turn Conversational Recommender Systems
Lu Zhang, Chen Li, Yu Lei, Zhu Sun, Guanfeng Liu
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Breaking the Silence: Whisper-Driven Emotion Recognition in AI Mental Support Models
Xinghua Qu, Zhu Sun, Shanshan Feng, Caishun Chen, Tian Tian
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Configurable Fairness for New Item Recommendation Considering Entry Time of Items
Huizhong Guo, Dongxia Wang, Zhu Sun, Haonan Zhang, Jinfeng Liu, Jie Zhang
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Disentangled Multi-interest Representation Learning for Sequential Recommendation
Yingpeng Du, Ziyan Wang, Zhu Sun, Yining Ma, Hongzhi Liu, Jie Zhang
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