cs.IR · 2026-06-08 · No. 17

Information Retrieval, 2026-06-08.

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

01 — The papers

4 entries
  1. 01

    Bradley-Terry Rankings for Recommender Systems Across Dataset Taxonomies

    Ekaterina Grishina, Stepan Kuznetsov, Askar Tsyganov, Ilya Ivanov, Daria Korovaitceva, Margarita Rusanova, Uliana...

    cs.IR · cs.LG · stat.ML

    The ranking of recommendation algorithms is a challenging problem since model performance is sensitive to dataset characteristics such as sparsity, sequential structure, and scale. This drives a demand for a proper methodology for fair comparison between algorithms. Naive aggregation of performance metrics (e.g., averaging NDCG over benchmarks) can yield misleading rankings, undermining practical selection. To address this problem, we...

    arxiv.org/abs/2606.07492 · PDF

  2. 02

    PaperFlow: Profiling, Recommending, and Adapting Across Daily Paper Streams

    Fuqiang Wang, Song Tan, Zheng Guo, Jiaohao Fu, Xinglong Xu, Bihui Yu, Jie Dong, Zheng Sun, Siyuan Li, Jingxuan Wei, Cheng Tan

    cs.IR · cs.AI

    Scientific paper recommendation is typically evaluated as static ranking over a fixed candidate set, yet real scientific reading unfolds as a daily, longitudinal process in which interests shift and feedback accumulates. We introduce PaperFlow, a framework that organizes it into three coupled stages: Profiling, which constructs and maintains a structured, inspectable scholarly profile from heterogeneous cold-start evidence; Recommending,...

    arxiv.org/abs/2606.07454 · PDF

  3. 03

    FLOWREADER: Min-Cost Flow Optimization for Multi-Modal Long Document Q&A

    Ambuj Mehrish, Sebatiano Vascon

    cs.IR · cs.LG

    Long, multimodal documents force retrieval-augmented systems to assemble answers from evidence fragmented across text, tables, and slides broken across cells in a long table, spread over multiple slides, or split between a figure and its discussion. Top-$k$ chunk retrieval treats each fragment independently and cannot represent how evidence connects. We introduce FLOWREADER, which reframes evidence assembly as a min-cost flow problem on a...

    arxiv.org/abs/2606.07235 · PDF

  4. 04

    Mind the Gap: Bridging Behavioral Silos with LLMs in Multi-Vertical Recommendations

    Nimesh Sinha, Raghav Saboo, Martin Wang, Sudeep Das

    cs.IR · cs.AI

    In multi-vertical e-commerce platforms like DoorDash, relatively newer product verticals such as grocery and retail present a significant opportunity for personalization innovation. A key challenge lies in solving the "cold start" problem for users. This paper introduces a novel framework for enhancing recommendation quality by transferring knowledge from data-rich verticals (e.g., restaurants at DoorDash) to data-sparse ones. We leverage...

    arxiv.org/abs/2606.06779 · PDF

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