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English(EN) Contrastive Learning for Aspect Representation towards Explainable Recommendation

新的CLARER模型提高了推荐的准确性和可解释性

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了CLARER，一种新的推荐模型，它结合了用户评分和从文本评论中提取的方面特征。该模型使用多层感知器处理基于评分的特征，并使用带有对比学习的Transformer编码器处理基于方面特征。然后，使用Transformer解码器为推荐生成解释。在基准数据集上的实验表明，CLARER在推荐准确性和解释质量方面均优于现有方法。 AI

影响 通过整合基于方面特征来提高准确性和可解释性，为改进推荐系统引入了一种新颖的方法。

排序理由 该集群包含一篇详细介绍新模型及其实验结果的学术论文。

在 arXiv cs.IR (Information Retrieval) 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新的CLARER模型提高了推荐的准确性和可解释性

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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Emrul Hasan, Chen Ding ·

    面向可解释推荐的对比学习用于方面表示

    arXiv:2610.07761v1 Announce Type: cross Abstract: In this work, we propose a novel recommendation model, CLARER (Contrastive Learning for Aspect Representation towards Explainable Recommendation) that integrates aspect features learned from textual reviews with rating information…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Chen Ding ·

    面向可解释推荐的对比学习用于方面表示

    In this work, we propose a novel recommendation model, CLARER (Contrastive Learning for Aspect Representation towards Explainable Recommendation) that integrates aspect features learned from textual reviews with rating information to improve the accuracy and explainability of rec…

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