32 citations · 46 across the 9 of their papers we have counts for
9 papers
KuaiLive-M3: A Multi-Modal, Multi-Domain, and Multi-Feedback Dataset for Live Streaming Recommendation
Ke Guo, Changle Qu, Jiayaqi Cheng +7
Existing public live streaming datasets suffer from three major limitations: they provide limited access to temporally evolving multimodal live content, overlook users' cross-domai…
KuaiLive: A Real-time Interactive Dataset for Live Streaming Recommendation
Changle Qu, Sunhao Dai, Ke Guo +7
Live streaming platforms have become a dominant form of online content consumption, offering dynamically evolving content, real-time interactions, and highly engaging user experien…
Revisiting Clustering of Neural Bandits: Selective Reinitialization for Mitigating Loss of Plasticity
Zhiyuan Su, Sunhao Dai, Xiao Zhang
Clustering of Bandits (CB) methods enhance sequential decision-making by grouping bandits into clusters based on similarity and incorporating cluster-level contextual information,…
Perplexity Trap: PLM-Based Retrievers Overrate Low Perplexity Documents
Haoyu Wang, Sunhao Dai, Haiyuan Zhao +6
Previous studies have found that PLM-based retrieval models exhibit a preference for LLM-generated content, assigning higher relevance scores to these documents even when their sem…
Length-Induced Embedding Collapse in PLM-based Models
Yuqi Zhou, Sunhao Dai, Zhanshuo Cao +2
Text embeddings from PLM-based models enable a wide range of applications, yet their performance often degrades on longer texts. In this paper, we introduce a phenomenon we call Le…
ReCODE: Modeling Repeat Consumption with Neural ODE
Sunhao Dai, Changle Qu, Sirui Chen +2
In real-world recommender systems, such as in the music domain, repeat consumption is a common phenomenon where users frequently listen to a small set of preferred songs or artists…
FairSync: Ensuring Amortized Group Exposure in Distributed Recommendation Retrieval
Chen Xu, Jun Xu, Yiming Ding +2
In pursuit of fairness and balanced development, recommender systems (RS) often prioritize group fairness, ensuring that specific groups maintain a minimum level of exposure over a…
UOEP: User-Oriented Exploration Policy for Enhancing Long-Term User Experiences in Recommender Systems
Changshuo Zhang, Sirui Chen, Xiao Zhang +3
Reinforcement learning (RL) has gained traction for enhancing user long-term experiences in recommender systems by effectively exploring users' interests. However, modern recommend…
Neural Retrievers are Biased Towards LLM-Generated Content
Sunhao Dai, Yuqi Zhou, Liang Pang +6
Recently, the emergence of large language models (LLMs) has revolutionized the paradigm of information retrieval (IR) applications, especially in web search, by generating vast amo…