8 citations · 8 across the 4 of their papers we have counts for
5 papers · 1 filter
HypRQ-VAE: Hyperbolic Item Indexing for Long-Tail-Aware Generative Recommender Systems
Longfeng Wu, Tong Zeng, Giovanni Seni +8
Sequential recommender systems model user behavior as item ID sequences, while recent generative methods cast recommendation as a language modeling task using large language models…
Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search
Longfeng Wu, Yao Zhou, Tong Zeng +5
Recommender systems are vital in helping users navigate vast amounts of information, offering personalized suggestions and effective explanations for these recommendations. While p…
Rich-Media Re-Ranker: A User Satisfaction-Driven LLM Re-ranking Framework for Rich-Media Search
Zihao Guo, Ligang Zhou, Zeyang Tang +5
Re-ranking plays a crucial role in modern information search systems by refining the ranking of initial search results to better satisfy user information needs. However, existing m…
MBCAL: Sample Efficient and Variance Reduced Reinforcement Learning for Recommender Systems
Fan Wang, Xiaomin Fang, Lihang Liu +2
In recommender systems such as news feed stream, it is essential to optimize the long-term utilities in the continuous user-system interaction processes. Previous works have proved…
Sequential Evaluation and Generation Framework for Combinatorial Recommender System
Fan Wang, Xiaomin Fang, Lihang Liu +5
In the combinatorial recommender systems, multiple items are fed to the user at one time in the result page, where the correlations among the items have impact on the user behavior…