Cost-efficiency Analysis in Deep Relation-Enhanced Graph Attention Networks for Social Recommender Systems

• Item type: National Convention(1)
• 公開日: 2023-02-16
• タイトル: Cost-efficiency Analysis in Deep Relation-Enhanced Graph Attention Networks for Social Recommender Systems
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: データとウェブ
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_5794
• 資源タイプ: conference paper
• 著者所属: 九大
• 著者所属: 九大
• 著者所属: 九大
• 著者所属: 九大
• 著者所属: 九大
• 著者所属: 九大
• 著者名: 楊, 添元; 陳, 宇; 任, 宝峰; 姚, 承佐; 徐, 飛克; 木實, 新一
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: The central problem of a recommender system is how to represent the user and item embedding. To explore the user-item interaction, one of the traditional methods is Collaborative Filtering(CF), which learns user and item embeddings based on historical user-item interactions. However, the performance of CF is limited due to the sparseness of user-item interaction, also CF methods ignore the user’s social connection, which also affect user preferences. Social recommendation leverages social connection to alleviate data sparsity in the recommender system. We argue that attention mechanisms in Graph Neural Networks (GNNs) can effectively incorporate social information to characterize the social property of the users and improve the performance of recommender systems.In this paper, we discuss cost-efficiency of deep relation-enhanced graph attention networks for social recommendation systems. In particular, we present our experiments on DREANRec to discuss whether having the attention mechanism in this module gives better cost-performance. Extensive experiments were implemented to prove the effectiveness of our approach.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00349328
• 書誌情報: 第85回全国大会講演論文集; 巻 2023, 号 1, p. 489-490, 発行日 2023-02-16
• 出版者 / 言語: ja
• 出版者: 情報処理学会