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CHAP framework enhances personalized generative retrieval with hierarchical alignment

Researchers have introduced CHAP, a novel framework for personalized generative retrieval that addresses limitations in current systems. CHAP utilizes a Hierarchical Semantic Alignment module to better match dynamic query intents with static item representations across multiple granularities. It also incorporates user behavior modeling and a Residual Cascading Generation mechanism to improve inference speed and reduce information loss. Experiments on public and proprietary datasets, including online A/B tests, have shown CHAP's effectiveness and practical value. AI

IMPACT This framework could improve the efficiency and personalization of search and recommendation systems.

RANK_REASON The cluster contains a research paper detailing a new framework for generative retrieval.

Read on arXiv cs.IR (Information Retrieval) →

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CHAP framework enhances personalized generative retrieval with hierarchical alignment

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The cluster contains a research paper detailing a new framework for generative retrieval.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Gaoming Zhang, Angqing Jiang, Jianchun Song, Kena Qi, Dayao Chen, Wei Lin, Defu Lian ·

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

    arXiv:2608.30553v1 Announce Type: cross Abstract: 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 conten…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Defu Lian ·

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

    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 …