DREANRec: Deep Relation Enhanced Attention Networks for Social Recommendation

• Item type: National Convention(1)
• 公開日: 2023-02-16
• タイトル: DREANRec: Deep Relation Enhanced Attention Networks for Social Recommendation
• 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: 陳, 宇; 楊, 添元; 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: Nowadays, Recommender System (RS) has become increasingly popular and essential in daily life. However, existing mainstream approaches consider only the user’s interests and the attributes of the item, ignoring the user’s social connections and the fact that social connections could influence the user’s choices. This can cause the recommended results to have problems called filter bubbles. We argue that Graph Neural Networks(GNNs) are highly suitable for recommender systems since most of the data in recommender systems can be represented as graph structures.In this paper, we propose DREANRec(Deep Relation Enhanced Attention Networks for Social Recommendation), a novel graph neural network, which effectively incorporates social information among users and considers the heterogeneous strength of social relations and latent item-item relations through the attention mechanism.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. 487-488, 発行日 2023-02-16
• 出版者 / 言語: ja
• 出版者: 情報処理学会