cs.IR · 2026-08-05 · No. 75

Information Retrieval, 2026-08-05.

3 new papers in cs.IR. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

3 entries
  1. 01

    Conditionally Identifiable Latent-Environment Modeling for Out-of-Distribution Recommendation

    Qianqian Wang, Wenwu Gong, Yunshan Li, Zhenqing Wu, Ruili Wang, Lili Yang

    cs.IR · cs.LG

    Out-of-distribution (OOD) recommendation is vulnerable to preference shifts induced by a latent environment. Existing methods can infer latent states from logged interactions, yet the statistical meaning of the latent environment and its effect on preference remain underdetermined. We formulate this task as conditionally identifiable risk-aware recommendation (CI-RR) and propose Conditionally Identifiable Latent-Environment Recommendation...

    arxiv.org/abs/2608.03647 · PDF

  2. 02

    Training Documents Reranker with Search Rubrics for Deep Research Agent

    Wenhan Liu, Yu Lu, Qiaolin Xia, Hui Xu, Tong Zhao, Jian Xi, Yutao Zhu, Haijin Liang, Haibo Shi, Hao Wang, Zhicheng Dou

    cs.IR · cs.AI · cs.CL

    Retrieval systems help deep research agents generate high-quality answers by providing relevant documents. However, existing retrievers typically select documents through relevance matching, while individually well-matched top-$k$ documents may not form a \textit{set} that satisfies the complex information needs of an agent query (\eg, diverse, concise and authoritative documents). In this paper, we propose search-oriented rubrics that...

    arxiv.org/abs/2608.03527 · PDF

  3. 03

    LLM-Derived Priors for Thompson Sampling in Cold-Start Comment Recommendation

    Eugene Lee, Oseong Choi, Byungsoo Kang, Taeyeong Jang

    cs.IR · cs.LG

    Multi-armed bandit algorithms, especially Thompson sampling, are widely used in online recommendation. Despite their ability to adapt from online feedback, these methods often suffer from cold-start limitations when newly introduced arms have little or no interaction history. In our setting, the candidate arms are user-generated textual comments, whose semantic content can reveal a title's appeal before sufficient interaction feedback is...

    arxiv.org/abs/2608.03382 · PDF

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