cs.IR · 2026-08-18 · No. 88

Information Retrieval, 2026-08-18.

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

01 — The papers

5 entries
  1. 01

    UniDot: A Unified Network for Sequence Modeling and Feature Interaction in Large-scale Recommendation

    Rongcheng Lin, Yan Sun, Jamey Zhang, Guanglei Xiong, Ivan Ji, Xianjie Chen, Shujian Bu

    cs.IR · cs.AI

    Industrial recommenders rely on two model families that have evolved largely independently: feature-interaction models over multi-field user/item features, and sequential models over user-behavior histories. Production systems couple them only loosely. To unify the two, we present UniDot, a novel architecture for post-click conversion prediction built from the factorization-machine (FM) point of view: the embedding inner product---which...

    arxiv.org/abs/2608.16797 · PDF

  2. 02

    POI Recommendation with LLM-Augmented Multi-Graph Learning and Contrastive Alignment

    Burak Tamer, Wolfram Höpken, Zehui Wang

    cs.IR · cs.LG

    Point-of-interest (POI) recommendation models based on graph neural networks achieve strong performance by propagating collaborative signals over user-item interactions, yet they struggle with the cold-start problem, where items with few or no interactions are not represented. In this paper, we propose LLM-augmented Multi-Graph Contrastive Learning (LLM-MGCL), a multi-graph neural network that uses semantic and spatial information about items...

    arxiv.org/abs/2608.16407 · PDF

  3. 03

    Static Pruning Across Sparse Retrieval Regimes: What Transfers, What Breaks, and What Still Helps

    Zirui Song, Yuye Zhu, Yang Yang

    cs.IR · cs.AI

    Static pruning is widely used to accelerate sparse neural retrieval, yet existing studies each validate their conclusions within a single custom pipeline, leaving it unclear which findings transfer to modern engines with different index organizations and dynamic pruning mechanisms. We present the first cross-engine pruning portability study, evaluating static pruning strategies across three engines - a controlled C++ pipeline (exhaustive...

    arxiv.org/abs/2608.16309 · PDF

  4. 04

    Decoupled Temporal Encoding for Generative Recommendation

    Pengfei Jia, Jingjian Wang, Jingmao Li, Ge Zhang, Feng Shi

    cs.IR · cs.AI

    Positional encoding is a fundamental component of Transformer-based generative recommendation models, where user histories are modeled as autoregressive item sequences. Most positional encoding methods are inherited from natural language processing and mainly represent discrete item order. However, recommendation sequences go beyond ordered lists, as timestamps and temporal effects also shape item relations. Our work is motivated by a...

    arxiv.org/abs/2608.16274 · PDF

  5. 05

    Domain-Specific Text Embedding Models for Entity Resolution

    Khajesh Sapram, Srivardhani Raju, Kishore Konda

    cs.IR · cs.AI · cs.LG

    General-purpose text embedding models are designed to capture semantic similarity but are not optimised for distinguishing entity records that represent the same real-world business or person. This limitation affects applications such as entity resolution and duplicate record retrieval, where small textual differences may either preserve or change identity. This paper investigates whether domain-specific triplet fine-tuning can adapt...

    arxiv.org/abs/2608.16161 · PDF

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