{"type":"ScholarlyArticle","title":"SeeDRec: Sememe-based Diffusion for Sequential Recommendation","authors":[{"name":"Haokai Ma","slug":"haokai-ma","given":"Haokai","family":"Ma","affiliation":"School of Software , Shandong University , China"},{"name":"Ruobing Xie","slug":"ruobing-xie","given":"Ruobing","family":"Xie","affiliation":"Tencent , China"},{"name":"Lei Meng","slug":"lei-meng","given":"Lei","family":"Meng","affiliation":"School of Software , Shandong University , China; Shandong Research Institute of Industrial Technology , China"},{"name":"Yimeng Yang","slug":"yimeng-yang","given":"Yimeng","family":"Yang","affiliation":"School of Software , Shandong University , China"},{"name":"Xingwu Sun","slug":"xingwu-sun","given":"Xingwu","family":"Sun","affiliation":"Tencent , China"},{"name":"Zhanhui Kang","slug":"zhanhui-kang","given":"Zhanhui","family":"Kang","affiliation":"Tencent , China"}],"abstract":"Inspired by the power of Diffusion Models (DM) verified in various fields, some pioneering works have started to explore DM in recommendation. However, these prevailing endeavors commonly implement diffusion on item indices, leading to the increasing time complexity, the lack of transferability, and the inability to fully harness item semantic information. To tackle these challenges, we propose SeeDRec, a sememe-based diffusion framework for sequential recommendation (SR). Specifically, inspired by the notion of sememe in NLP, SeeDRec first defines a similar concept of recommendation sememe to represent the minimal interest unit and upgrades the specific diffusion objective from the item level to the sememe level. With the Sememe-to-Interest Diffusion Model (S2IDM), SeeDRec can accurately capture the user's diffused interest distribution learned from both local interest evolution and global interest generalization while maintaining low computational costs. Subsequently, an Interest-aware Prompt-enhanced (IPE) strategy is proposed to better guide each user's sequential behavior modeling via the learned user interest distribution. Extensive experiments on nine SR datasets and four cross-domain SR datasets verify its effectiveness and universality. The code is available in https://github.com/hulkima/SeeDRec.","doi":"10.24963/ijcai.2024/251","url":"https://ijcai.org/proceedings/2024/251","pdf":"https://ijcai.org/proceedings/2024/0251.pdf","markdown":"https://ijcai.org/proceedings/2024/251.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/251","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Data Mining","track":"Main Track","number":251,"pages":{"first":2270,"last":2278},"keywords":["Data Mining: DM: Recommender systems"],"videos":[],"references":[{"position":1,"raw":"[Austin et al., 2021] Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne Van Den Berg. 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