{"type":"ScholarlyArticle","title":"Modeling Multi-Purpose Sessions for Next-Item Recommendations via Mixture-Channel Purpose Routing Networks","authors":[{"name":"Shoujin Wang","slug":"shoujin-wang","given":"Shoujin","family":"Wang","affiliation":"Department of Computing , Macquarie University"},{"name":"Liang Hu","slug":"liang-hu","given":"Liang","family":"Hu","affiliation":"Advanced Analytics Institute , University of Technology Sydney; University of Shanghai for Science and Technology"},{"name":"Yan Wang","slug":"yan-wang","given":"Yan","family":"Wang","affiliation":"Department of Computing , Macquarie University"},{"name":"Quan Z. Sheng","slug":"quan-z-sheng","given":"Quan Z.","family":"Sheng","affiliation":"Department of Computing , Macquarie University"},{"name":"Mehmet Orgun","slug":"mehmet-orgun","given":"Mehmet","family":"Orgun","affiliation":"Department of Computing , Macquarie University"},{"name":"Longbing Cao","slug":"longbing-cao","given":"Longbing","family":"Cao","affiliation":"Advanced Analytics Institute , University of Technology Sydney"}],"abstract":"A session-based recommender system (SBRS) suggests the next item by modeling the dependencies between items in a session. Most of existing SBRSs assume the items inside a session are associated with one (implicit) purpose. However, this may not always be true in reality, and a session may often consist of multiple subsets of items for different purposes (e.g., breakfast and decoration). Specifically, items (e.g., bread and milk) in a subsethave strong purpose-specific dependencies whereas items (e.g., bread and vase) from different subsets have much weaker or even no dependencies due to the difference of purposes. Therefore, we propose a mixture-channel model to accommodate the multi-purpose item subsets for more precisely representing a session. Filling gaps in existing SBRSs, this model recommends more diverse items to satisfy different purposes. Accordingly, we design effective mixture-channel purpose routing networks (MCPRN) with a purpose routing network to detect the purposes of each item and assign it into the corresponding channels. Moreover, a purpose specific recurrent network is devised to model the dependencies between items within each channel for a specific purpose. The experimental results show the superiority of MCPRN over the state-of-the-art methods in terms of both recommendation accuracy and diversity.","doi":"10.24963/ijcai.2019/523","url":"https://ijcai.org/proceedings/2019/523","pdf":"https://ijcai.org/proceedings/2019/0523.pdf","markdown":"https://ijcai.org/proceedings/2019/523.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/523","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":523,"pages":{"first":3771,"last":3777},"keywords":["Machine Learning: Recommender Systems","Uncertainty in AI: Sequential Decision Making","Knowledge Representation and Reasoning: Preference Modelling and Preference-Based Reasoning","Humans and AI: Personalization and User Modeling"],"videos":[],"references":[{"position":1,"raw":"[Chen et al., 2018] Xu Chen, Hongteng Xu, and et al. 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