TANET 2024-Contrastive Learning Recommendation Systems with Time-Variant Objectives
學年 113
學期 1
發表日期 2024-10-26
作品名稱 TANET 2024-Contrastive Learning Recommendation Systems with Time-Variant Objectives
作品名稱（其他語言）
著者 夏肇毅
作品所屬單位
出版者
會議名稱 TANET 2024
會議地點 台北市，台灣
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
關鍵字 Recommendation Systems;Contrastive Learning;Time Variant Objectives;TVO, GCN
語言 en
收錄於
會議性質 國內
校內研討會地點 無
研討會時間 20241026~20241026
通訊作者 夏肇毅
國別 TWN
公開徵稿
出版型式
出處 TANET 2024 論文集
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/126441 )