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20232026
most citedNeural Retrievers are Biased Towards LLM-Generated Content

32 citations · 46 across the 9 of their papers we have counts for

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6 papers · 1 filter

cs.IR2026

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…

cs.IR2025

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…

cs.IR2024★ 11 cited

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…

cs.IR2024

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…

cs.IR2024

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…

cs.IR2023★ 32 cited

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…