cs.IR · 2026-09-01 · No. 102

Information Retrieval, 2026-09-01.

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

    Learning from What You Retrieve: Online RL Fine-Tuning for Semantic Retrieval

    Shaowei Wei, Chong Huang, Songtao Fang, Jin Zhang, Zhuojun Wang, Chengfu Huo

    cs.IR · cs.AI

    In large-scale e-commerce retrieval, dual-encoder retrievers are op- timized for contrastive similarity, whereas downstream rerankers capture finer-grained relevance preferences; this objective mis- match limits end-to-end retrieval quality. Reinforcement Learning offers a way to use reward-model feedback for retriever adaptation, but we observe that standard policy-gradient updates can degrade embedding geometry, especially when the document...

    arxiv.org/abs/2608.30753 · PDF

  2. 02

    Generative Retrieval for E-commerce: Jointly Learning Embedding and Codebook with Same Product Cluster

    Songtao Fang, Zihao Xu, Shaowei Wei, Jin Zhang, Zhuojun Wang

    cs.IR · cs.AI

    With the development of large language models (LLMs), generative retrieval is becoming increasingly important in e-commerce scenarios. Current mainstream approaches typically use a two-stage training strategy: first train a product embedding model, and then learn a codebook that maps embeddings to product IDs. This cascaded approach suffers from two major issues: (1) error accumulation-if the embedding model in the first stage produces biased...

    arxiv.org/abs/2608.30606 · PDF

  3. 03

    Preference Shapes Relevance: Cross-component Hierarchical Semantic Alignment for Personalized Generative Retrieval

    Gaoming Zhang, Angqing Jiang, Jianchun Song, Kena Qi, Dayao Chen, Wei Lin, Defu Lian

    cs.IR · cs.AI

    Generative Retrieval (GR) has emerged as a promising paradigm by mapping queries directly to Semantic IDs (SIDs) with powerful representation capabilities for candidate items. However, existing SIDs derived solely from item content create a semantic gap, failing to align dynamic query intents with static item representations. Furthermore, current generative paradigms rarely model user behavior sequences and are always bottlenecked by the high...

    arxiv.org/abs/2608.30553 · PDF

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