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HD-Rec framework enhances cross-domain recommendations with hierarchical quantization

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced HD-Rec, a novel generative recommendation framework designed to tackle the complexities of cross-domain recommendation. This system utilizes a hierarchical domain-aware quantizer to create semantic identifiers, combining shared coarse-level codebooks with adaptive fine-level ones. Additionally, it incorporates a domain-adaptive sparse mixture-of-experts module for dynamic expert selection and a consistency objective to refine multi-token item representations. Experiments on public benchmarks indicate that HD-Rec outperforms existing sequential, generative, and cross-domain recommendation methods. AI

IMPACT This research could lead to more accurate and personalized recommendations across different platforms and services.

RANK_REASON The cluster contains a research paper detailing a new method for generative cross-domain recommendation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

HD-Rec framework enhances cross-domain recommendations with hierarchical quantization

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The cluster contains a research paper detailing a new method for generative cross-domain recommendation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Xiangyu Zhao ·

    Hierarchical Quantization with Domain-Adaptive Sparse Routing for Generative Cross-Domain Recommendation

    Generative Recommendation (GenRec) represents a promising paradigm that achieves remarkable empirical success by encoding items as compact Semantic IDs (SIDs) and modeling user behavior via next-token prediction across diverse recommendation scenarios. Extending this paradigm to …

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