19 citations · 22 across the 12 of their papers we have counts for
12 papers
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…
GSE: Evaluating Sticker Visual Semantic Similarity via a General Sticker Encoder
Heng Er Metilda Chee, Jiayin Wang, Zhiqiang Guo +2
Stickers have become a popular form of visual communication, yet understanding their semantic relationships remains challenging due to their highly diverse and symbolic content. In…
How Far Can LLMs Improve from Experience? Measuring Test-Time Learning Ability in LLMs with Human Comparison
Jiayin Wang, Zhiquang Guo, Weizhi Ma +1
As evaluation designs of large language models may shape our trajectory toward artificial general intelligence, comprehensive and forward-looking assessment is essential. Existing…
Small Stickers, Big Meanings: A Multilingual Sticker Semantic Understanding Dataset with a Gamified Approach
Heng Er Metilda Chee, Jiayin Wang, Zhiqiang Guo +2
Stickers, though small, are a highly condensed form of visual expression, ubiquitous across messaging platforms and embraced by diverse cultures, genders, and age groups. Despite t…
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…
A User-Centric Multi-Intent Benchmark for Evaluating Large Language Models
Jiayin Wang, Fengran Mo, Weizhi Ma +3
Large language models (LLMs) are essential tools that users employ across various scenarios, so evaluating their performance and guiding users in selecting the suitable service is…
Understanding User Experience in Large Language Model Interactions
Jiayin Wang, Weizhi Ma, Peijie Sun +2
In the rapidly evolving landscape of large language models (LLMs), most research has primarily viewed them as independent individuals, focusing on assessing their capabilities thro…
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…