{"type":"ScholarlyArticle","title":"Sequential Recommendation with Probabilistic Logical Reasoning","authors":[{"name":"Huanhuan Yuan","slug":"huanhuan-yuan","given":"Huanhuan","family":"Yuan","affiliation":"Soochow University"},{"name":"Pengpeng Zhao","slug":"pengpeng-zhao","given":"Pengpeng","family":"Zhao","affiliation":"Soochow University"},{"name":"Xuefeng Xian","slug":"xuefeng-xian","given":"Xuefeng","family":"Xian","affiliation":"Suzhou Vocational University"},{"name":"Guanfeng Liu","slug":"guanfeng-liu","given":"Guanfeng","family":"Liu","affiliation":"Macquarie University"},{"name":"Yanchi Liu","slug":"yanchi-liu","given":"Yanchi","family":"Liu","affiliation":"Rutgers University"},{"name":"Victor S. Sheng","slug":"victor-s-sheng","given":"Victor S.","family":"Sheng","affiliation":"Texas Tech University"},{"name":"Lei Zhao","slug":"lei-zhao","given":"Lei","family":"Zhao","affiliation":"Soochow University"}],"abstract":"Deep learning and symbolic learning are two frequently employed methods in Sequential Recommendation (SR). Recent neural-symbolic SR models demonstrate their potential to enable SR to be equipped with concurrent perception and cognition capacities. However, neural-symbolic SR remains a challenging problem due to open issues like representing users and items in logical reasoning. In this paper, we combine the Deep Neural Network (DNN) SR models with logical reasoning and propose a general framework named Sequential Recommendation with Probabilistic Logical Reasoning (short for SR-PLR). This framework allows SR-PLR to benefit from both similarity matching and logical reasoning by disentangling feature embedding and logic embedding in the DNN and probabilistic logic network. To better capture the uncertainty and evolution of user tastes, SR-PLR embeds users and items with a probabilistic method and conducts probabilistic logical reasoning on users' interaction patterns.  Then the feature and logic representations learned from the DNN and logic network are concatenated to make the prediction. Finally, experiments on various sequential recommendation models demonstrate the effectiveness of the SR-PLR. Our code is available at https://github.com/Huanhuaneryuan/SR-PLR.","doi":"10.24963/ijcai.2023/270","url":"https://ijcai.org/proceedings/2023/270","pdf":"https://ijcai.org/proceedings/2023/0270.pdf","markdown":"https://ijcai.org/proceedings/2023/270.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/270","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Data Mining","track":"Main Track","number":270,"pages":{"first":2432,"last":2440},"keywords":["Data Mining: DM: Collaborative filtering","Data Mining: DM: Recommender systems"],"videos":[],"references":[{"position":1,"raw":"[Chen et al., 2021] Hanxiong Chen, Shaoyun Shi, Yunqi Li, and Yongfeng Zhang. 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