cs.IR · 2026-08-28 · No. 98

Information Retrieval, 2026-08-28.

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

    Scaling Graph Neural Networks for Friend Recommendation: Multi-Hash User Embeddings and Temporal Neighbor Sampling

    Maksim Utushkin, Andrei Ovsiannikov, Alexander D'yakonov

    cs.IR · cs.LG · cs.SI

    Friend recommendation is inherently graph-structured: the relevance of a potential connection depends on multi-hop social context rather than user attributes alone. However, deploying message-passing GNNs on a production-scale social graph with hundreds of millions of users and tens of billions of edges requires addressing numerous modeling and systems challenges. We present a scalable end-to-end GNN ranking system for production social...

    arxiv.org/abs/2608.27413 · PDF

  2. 02

    Stageboost: Recommending Signals Based on Counterfactual Estimation

    Darpan Singhal, Matan Mandelbrod, Tal Franji, Manasa Kolla, Vipul Gaba, Yuri Brovman

    cs.IR · cs.AI

    Signals are short textual or visual snippets displayed on the eBay View-Item (VI) page, providing additional, contextual information for users about the viewed item. The aim of displaying these signals is to facilitate intelligent purchase and to incentivize engagement. In this paper, we present a 2 stage xgboost based model that optimally populates the VI page with signals. This approach has shown a 0.08% lift in overall GMB (Gross...

    arxiv.org/abs/2608.27366 · PDF

  3. 03

    When Memory Takes Gradients: Collaborative Vector Memory for Agentic Recommender Systems

    Hanchong Chen, Xing Tang, Lingjie Li, Xiongfeng Shan, Xiuqiang He

    cs.IR · cs.AI

    Agentic recommender systems ground each decision of a large language model (LLM) in a persistent memory of the user, and in existing agents that memory is text: a narrative written and maintained by further LLM calls. Text limits this memory in two ways. It is updated one rewrite at a time, so exploiting the full interaction history is prohibitively expensive; and collaborative evidence, graded similarity over an entire catalog, does not...

    arxiv.org/abs/2608.26895 · PDF

  4. 04

    hoBIT: A Profile-Aware Retrieval-Augmented Chatbot for University Academic Advising

    Yoonseo Kim, Seongmin Lee, Joongheon Kim, SeongKu Kang

    cs.IR · cs.LG

    In university academic advising, identical questions can require different answers depending on a student's department, admission cohort, and degree program, causing profile-blind retrievers to surface plausible but inapplicable evidence. We present proFILL, a method for transforming hoBIT, our college's current rule-based advising chatbot, into a profile-aware retrieval-augmented generation (RAG) system. Rather than requiring a complete user...

    arxiv.org/abs/2608.26604 · PDF

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