cs.IR · 2026-09-28 · No. 127

Information Retrieval, 2026-09-28.

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

    Retail Product Search: A Practical Approach at Target

    Darshan Sonagara, Qujiaheng Zhang, Ankit Singh, Alex Li

    cs.IR · cs.LG

    Search is one of the most important features in e-commerce, directly driving customer engagement and business growth. A good product search system must show both relevant and desirable results. However, retail search presents unique challenges. User intent can range from exact matches to open-ended discovery. Search systems must also balance multiple goals, such as relevance, revenue, and profit, while keeping response times low. Traditional...

    arxiv.org/abs/2609.31498 · PDF

  2. 02

    AgentRecommender: LLM Agents Enable Customizable Recommender Systems on the User Side

    Ryoma Sato

    cs.IR · cs.AI · cs.DB · cs.DL

    Recommender systems have traditionally been developed for platforms. However, this has given rise to many phenomena that may be advantageous for platform lock-in but are a nuisance to users, such as clickbait, filter bubbles, and the spread of fake news. Recently, user-side recommender systems have been proposed as a new paradigm for solving this problem. If users deploy their own recommender systems, they are no longer at the mercy of the...

    arxiv.org/abs/2609.31166 · PDF

  3. 03

    SPADE: Escaping the Popularity-Similarity Frontier to Measure Serendipitous Recommendations

    Tobias Vente, Maarten Peirsman, Noah Daniëls, Hannu Toivonen, Bart Goethals

    cs.IR · cs.AI · cs.LG

    Recommender systems engineer serendipity to foster active exploration and break predictable consumption cycles. The problem with existing offline beyond-accuracy metrics is that they often either isolate historical similarity or global popularity. We aim to design an evaluation metric that examines similarity, popularity, and actual user relevance. To achieve this, we introduce SPADE (Serendipitous Pareto Distance Evaluation). SPADE maps all...

    arxiv.org/abs/2609.31164 · PDF

  4. 04

    KuaFu: Compressing Long User Behavior into Understanding at Billion Scale

    Jiahao Hui, Lin Zhu, Yishen Hu, Jingdong Shu, Zetai Jiang, Xining Ran, Ben Tan, Yeshou Cai, Gong Chen, Haijie Gu, Jie Jiang

    cs.IR · cs.CL · cs.LG

    Conversational agents, generative recommenders, and personalized advertising all rest on one capability: understanding each user from raw behavior. Prevailing industrial practice is task-specific: for each task, a relevant subsequence is extracted from the full history and a dedicated model trained on it. In production it hits two bottlenecks. First, even after filtering, a single-task sequence stays extremely long: content-interest...

    arxiv.org/abs/2609.31045 · PDF

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