DemiRec: Dynamic Evolutionary Multi-Interest Network for Sequential Recommendation

• Item type: SIG Technical Reports(1)
• 公開日: 2021-12-20
• タイトル: DemiRec: Dynamic Evolutionary Multi-Interest Network for Sequential Recommendation
• タイトル / 言語: en
• タイトル: DemiRec: Dynamic Evolutionary Multi-Interest Network for Sequential Recommendation
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: データベース処理
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: HDI Lab, Kyushu University
• 著者所属: HDI Lab, Kyushu University
• 著者所属(英): en
• 著者所属(英): HDI Lab, Kyushu University
• 著者所属(英): en
• 著者所属(英): HDI Lab, Kyushu University
• 著者名: Anyu, Cai; Shin'ichi, Konomi
• 著者名(英): Anyu, Cai; Shin'ichi, Konomi
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: In recommender systems, capturing the rich sequential information in historical interaction sequences is essential for user representation learning. However, most of the existing methods only focused on modeling the user's historical information into one fixed-length vector, which brings two limitations. First, one fixed-length vector is insufficient to model a user's varying interests. This simplified user modeling leads to monotonous recommendations, making users bored and trapped in the information cocoon. Second, since the user's interests are naturally dynamic and evolving, merely considering past information can only capture the user's outdated interests and predict the interest changes based on a fixed period (i.e., they model the elapsed times since the historical interactions into a same interval). As a result, they cannot give different predictions when users access the system after different time intervals, resulting in suboptimal recommendation performance. In this paper, we seek to explicitly model the users' diverse interests and elapsed time within a sequential interests modeling framework to explore the causality between users' multi-interests and the influence of different elapsed times. We propose a Dynamic Evolutionary Multi-Interest network for sequential RECommendation (DemiRec), which models user's historical interactions into an interest sequence and predicts evolved interests based on sequential information and elapsed time. Extensive empirical studies on real-world datasets demonstrate that the proposed method significantly outperforms state-of-the-art multi-interest baselines.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: In recommender systems, capturing the rich sequential information in historical interaction sequences is essential for user representation learning. However, most of the existing methods only focused on modeling the user's historical information into one fixed-length vector, which brings two limitations. First, one fixed-length vector is insufficient to model a user's varying interests. This simplified user modeling leads to monotonous recommendations, making users bored and trapped in the information cocoon. Second, since the user's interests are naturally dynamic and evolving, merely considering past information can only capture the user's outdated interests and predict the interest changes based on a fixed period (i.e., they model the elapsed times since the historical interactions into a same interval). As a result, they cannot give different predictions when users access the system after different time intervals, resulting in suboptimal recommendation performance. In this paper, we seek to explicitly model the users' diverse interests and elapsed time within a sequential interests modeling framework to explore the causality between users' multi-interests and the influence of different elapsed times. We propose a Dynamic Evolutionary Multi-Interest network for sequential RECommendation (DemiRec), which models user's historical interactions into an interest sequence and predicts evolved interests based on sequential information and elapsed time. Extensive empirical studies on real-world datasets demonstrate that the proposed method significantly outperforms state-of-the-art multi-interest baselines.
• 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: AN10112482
• 書誌情報: 研究報告データベースシステム（DBS）; 巻 2021-DBS-174, 号 8, p. 1-8, 発行日 2021-12-20
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-871X
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会