cs.IR · 2026-05-27 · No. 10

Information Retrieval, 2026-05-27.

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

    ICICLE: Expanding Retrieval with In-Context Documents

    Yu-Chen Den, Yung-Yu Shih, Zhi Rui Tam, Kuan-Yu Chen, Pu-Jen Cheng, Yun-Nung Chen, Eugene Yang

    cs.IR · cs.AI

    Generative retrieval (GR) maps queries directly to document identifiers (docids) using parametric knowledge, However, this design makes corpus expansion costly: adding new documents requires updating model parameters to encode new document-docid associations incurs repeated training and catastrophic forgetting of previously indexed documents. In this work, we revisit incremental GR as an in-context retrieval problem, where newly added...

    arxiv.org/abs/2605.26902 · PDF

  2. 02

    RAGEAR: Retrieval-Augmented Graph-Enhanced Academic Recommender

    Francesco Granata, Lorenzo Lamazzi, Misael Mongiovì, Francesco Poggi, Valeria Secchini

    cs.IR · cs.AI

    We present RAGEAR (Retrieval-Augmented Graph-Enhanced Academic Recommender), a neurosymbolic recommender system for academic course recommendation. RAGEAR combines dense retrieval over full lecture transcripts with a symbolic Knowledge Graph modelling courses, lessons, transcript chunks, credits, study plans, and curricular information. The Knowledge Graph supports symbolic filtering and contextualisation based on structured constraints, such...

    arxiv.org/abs/2605.26819 · PDF

  3. 03

    L2Rec: Towards Dual-View Understanding of LLMs for Personalized Recommendation

    Pingjun Pan, Tingting Zhou, Peiyao Lu, Tingting Fei, Hongxiang Chen, Chuanjiang Luo

    cs.IR · cs.AI

    Adapting large language models (LLMs) for personalized recommendation requires aligning their general-purpose capabilities with user-specific preferences while effectively leveraging both behavioral and semantic signals. Existing approaches typically integrate these signals at either the input level (e.g., injecting behavioral embeddings into the token space) or the output level (e.g., contrastive alignment of separate encoders), suffering...

    arxiv.org/abs/2605.26717 · PDF

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