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20192023
most citedControllable Multi-Objective Re-ranking with Policy Hypernetworks

25 citations · 72 across the 8 of their papers we have counts for

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8 papers · 1 filter

cs.IR2023

Generative Retrieval with Semantic Tree-Structured Item Identifiers via Contrastive Learning

Zihua Si, Zhongxiang Sun, Jiale Chen +7

The retrieval phase is a vital component in recommendation systems, requiring the model to be effective and efficient. Recently, generative retrieval has become an emerging paradig…

cs.IR202322 cited

Uncovering User Interest from Biased and Noised Watch Time in Video Recommendation

Haiyuan Zhao, Lei Zhang, Jun Xu +3

In the video recommendation, watch time is commonly adopted as an indicator of user interest. However, watch time is not only influenced by the matching of users' interests but als…

cs.IR2023

LTP-MMF: Towards Long-term Provider Max-min Fairness Under Recommendation Feedback Loops

Chen Xu, Xiaopeng Ye, Jun Xu +3

Multi-stakeholder recommender systems involve various roles, such as users, and providers. Previous work pointed out that max-min fairness (MMF) is a better metric to support weak…

cs.IR20234 cited

Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community

Qingyao Ai, Ting Bai, Zhao Cao +30

The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs. Recently, Large Language Mod…

cs.IR202325 cited

Controllable Multi-Objective Re-ranking with Policy Hypernetworks

Sirui Chen, Yuan Wang, Zijing Wen +6

Multi-stage ranking pipelines have become widely used strategies in modern recommender systems, where the final stage aims to return a ranked list of items that balances a number o…

cs.IR2023

KuaiSAR: A Unified Search And Recommendation Dataset

Zhongxiang Sun, Zihua Si, Xiaoxue Zang +5

The confluence of Search and Recommendation (S&R) services is vital to online services, including e-commerce and video platforms. The integration of S&R modeling is a highly intuit…