TANET 2024-Contrastive Learning Recommendation Systems with Time-Variant Objectives

淡江大學機構典藏 > 商管學院 > 管理科學學系暨研究所 > 會議論文 > Item 987654321/126441

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题名:	TANET 2024-Contrastive Learning Recommendation Systems with Time-Variant Objectives
作者:	夏肇毅
关键词:	Recommendation Systems;Contrastive Learning;Time Variant Objectives;TVO, GCN
日期:	2024-10-26
上传时间:	2024-10-28 12:05:46 (UTC+8)
出版者:	國立陽明交通大學
摘要:	Recommendation systems have seen significant advancements with the application of machine learning techniques, yet challenges remain in maintaining optimal performance throughout training. Contrastive learning, while effective in enhancing user and item representations, often suffers from performance degradation over time. In this paper, we present a novel approach that incorporates time-variant objectives (TVO) to address this issue. By integrating a scheduler with various time-variant functions into the contrastive learning framework, we dynamically balance the recommendation loss and contrastive loss during training. This method stabilizes model performance and mitigates the typical decline observed in traditional approaches. Our experimental results show that the TVO-enhanced model achieves more reliable and precise recommendations compared to existing methods. This approach offers a promising solution for improving the consistency and accuracy of contrastive learning-based recommendation systems. Keywords: Recommendation Systems, Contrastive Learning, Time Variant Objectives, TVO, GCN
關聯:	TANET 2024 論文集
显示于类别:	[管理科學學系暨研究所] 會議論文

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