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English(EN) LESSRec: Level-Specific History Modeling for Semantic ID-Based Recommendation

新的LESSRec模型增强了语义ID推荐

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种新的语义ID推荐系统方法LESSRec。该方法分别对不同语义层级的历史token序列进行建模，在预测时将注意力集中在最相关的层级，以避免干扰。LESSRec+通过为每个语义层级使用不同的编码器进一步优化了这一点，在保持参数效率的同时降低了计算负载。在公开数据集上的实验表明，LESSRec和LESSRec+显著提高了推荐的有效性和效率。 AI

排序理由 该条目是一篇研究论文，详细介绍了一种新的推荐系统模型。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.IR (Information Retrieval) 阅读 →

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新的LESSRec模型增强了语义ID推荐

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该条目是一篇研究论文，详细介绍了一种新的推荐系统模型。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Min Gao ·

    LESSRec：基于语义ID的逐级历史建模推荐

    Semantic ID-based recommendation represents each item as a sequence of semantic tokens obtained through residual quantization. This paradigm generates the next item's tokens autoregressively from coarse to fine, entailing each prediction attending to historical tokens at all sema…

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