19 citations · 22 across the 12 of their papers we have counts for
7 papers · 1 filter
SelfDR: Self-Distillation from Reasoning for LLM-Based Recommendation
Chumeng Jiang, Jiayin Wang, Xinjie Lin +3
Large Language Models (LLMs) have recently emerged as powerful backbones for recommendation. To better elicit their capabilities, reasoning has been widely incorporated to help LLM…
U-Sticker: A Large-Scale Multi-Domain User Sticker Dataset for Retrieval and Personalization
Heng Er Metilda Chee, Jiayin Wang, Zhiqiang Guo +3
Instant messaging with texts and stickers has become a widely adopted communication medium, enabling efficient expression of user semantics and emotions. With the increased use of…
Understanding Internal Representations of Recommendation Models with Sparse Autoencoders
Jiayin Wang, Xiaoyu Zhang, Weizhi Ma +2
Recommendation model interpretation aims to reveal the relationships between inputs, model internal representations and outputs to enhance the transparency, interpretability, and t…
Beyond Utility: Evaluating LLM as Recommender
Chumeng Jiang, Jiayin Wang, Weizhi Ma +4
With the rapid development of Large Language Models (LLMs), recent studies employed LLMs as recommenders to provide personalized information services for distinct users. Despite ef…
PerSRV: Personalized Sticker Retrieval with Vision-Language Model
Heng Er Metilda Chee, Jiayin Wang, Zhiqiang Guo +2
Instant Messaging is a popular means for daily communication, allowing users to send text and stickers. As the saying goes, "a picture is worth a thousand words", so developing an…
Large Language Models as Evaluators for Recommendation Explanations
Xiaoyu Zhang, Yishan Li, Jiayin Wang +4
The explainability of recommender systems has attracted significant attention in academia and industry. Many efforts have been made for explainable recommendations, yet evaluating…
Measuring Item Global Residual Value for Fair Recommendation
Jiayin Wang, Weizhi Ma, Chumeng Jiang +4
In the era of information explosion, numerous items emerge every day, especially in feed scenarios. Due to the limited system display slots and user browsing attention, various rec…