19 citations · 47 across the 22 of their papers we have counts for
23 papers
KUAISHOU Explorer LLM-Rec Challenge 2026: Reasoning Generative Recommendation
Jiangxia Cao, Hao Peng, Wenlong Xu +97
Generative recommendation, has been attracted a surge of attentions in industrial and academic research community, towards to build more smart system to build next-generation recom…
From Verbatim to Gist: Distilling Pyramidal Multimodal Memory via Semantic Information Bottleneck for Long-Horizon Video Agents
Niu Lian, Yuting Wang, Hanshu Yao +5
While multimodal large language models have demonstrated impressive short-term reasoning, they struggle with long-horizon video understanding due to limited context windows and sta…
BrandFusion: A Multi-Agent Framework for Seamless Brand Integration in Text-to-Video Generation
Zihao Zhu, Ruotong Wang, Siwei Lyu +2
The rapid advancement of text-to-video (T2V) models has revolutionized content creation, yet their commercial potential remains largely untapped. We introduce, for the first time,…
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…
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…
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…
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…
ReChorus2.0: A Modular and Task-Flexible Recommendation Library
Jiayu Li, Hanyu Li, Zhiyu He +4
With the applications of recommendation systems rapidly expanding, an increasing number of studies have focused on every aspect of recommender systems with different data inputs, m…
To Recommend or Not: Recommendability Identification in Conversations with Pre-trained Language Models
Zhefan Wang, Weizhi Ma, Min Zhang
Most current recommender systems primarily focus on what to recommend, assuming users always require personalized recommendations. However, with the widely spread of ChatGPT and ot…
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…
Sequential Recommendation with Latent Relations based on Large Language Model
Shenghao Yang, Weizhi Ma, Peijie Sun +4
Sequential recommender systems predict items that may interest users by modeling their preferences based on historical interactions. Traditional sequential recommendation methods r…
Common Sense Enhanced Knowledge-based Recommendation with Large Language Model
Shenghao Yang, Weizhi Ma, Peijie Sun +4
Knowledge-based recommendation models effectively alleviate the data sparsity issue leveraging the side information in the knowledge graph, and have achieved considerable performan…
A Situation-aware Enhancer for Personalized Recommendation
Jiayu Li, Peijie Sun, Chumeng Jiang +3
When users interact with Recommender Systems (RecSys), current situations, such as time, location, and environment, significantly influence their preferences. Situations serve as t…
Recommender for Its Purpose: Repeat and Exploration in Food Delivery Recommendations
Jiayu Li, Aixin Sun, Weizhi Ma +2
Recommender systems have been widely used for various scenarios, such as e-commerce, news, and music, providing online contents to help and enrich users' daily life. Different scen…
Intersectional Two-sided Fairness in Recommendation
Yifan Wang, Peijie Sun, Weizhi Ma +4
Fairness of recommender systems (RS) has attracted increasing attention recently. Based on the involved stakeholders, the fairness of RS can be divided into user fairness, item fai…
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…