2 citations · 3 across the 13 of their papers we have counts for
13 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…
Personalized Turn-Level User Conversation Satisfaction Benchmark
Zhefan Wang, Zhiqiang Guo, Weizhi Ma +3
User satisfaction with AI assistants is highly personalized: the same response may satisfy one user but disappoint another depending on what each user expects and what they have as…
APAO: Bridging the Training-Inference Gap in Generative Recommendation via Adaptive Prefix-Aware Optimization
Yuanqing Yu, Yifan Wang, Weizhi Ma +2
Generative recommendation has recently emerged as a promising paradigm for sequential recommendation. It formulates the task as an autoregressive generation process, predicting tok…
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…
Integrating LLM and Diffusion-Based Agents for Social Simulation
Xinyi Li, Zhiqiang Guo, Qinglang Guo +3
Large language models (LLMs) offer strong semantic reasoning capabilities for user modeling, but applying LLM-based simulation to an entire social network is computationally expens…
Human vs. Agent in Task-Oriented Conversations
Zhefan Wang, Ning Geng, Zhiqiang Guo +2
Task-oriented conversational systems are essential for efficiently addressing diverse user needs, yet their development requires substantial amounts of high-quality conversational…
Are Recommenders Self-Aware? Label-Free Recommendation Performance Estimation via Model Uncertainty
Jiayu Li, Ziyi Ye, Guohao Jian +4
Can a recommendation model be self-aware? This paper investigates the recommender's self-awareness by quantifying its uncertainty, which provides a label-free estimation of its per…
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…
Short Video Segment-level User Dynamic Interests Modeling in Personalized Recommendation
Zhiyu He, Zhixin Ling, Jiayu Li +5
The rapid growth of short videos has necessitated effective recommender systems to match users with content tailored to their evolving preferences. Current video recommendation mod…
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…
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…
StepTool: Enhancing Multi-Step Tool Usage in LLMs via Step-Grained Reinforcement Learning
Yuanqing Yu, Zhefan Wang, Weizhi Ma +4
Despite their powerful text generation capabilities, large language models (LLMs) still struggle to effectively utilize external tools to solve complex tasks, a challenge known as…