Modeling multi-purpose sessions for next-item recommendations via mixture-channel purpose routing networks

Shoujin Wang, Liang Hu, Yan Wang, Quan Z. Sheng, Mehmet Orgun, Longbing Cao

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

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 subset have 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. To address the shortcomings in existing SBRSs, this model recommends more diverse items to satisfy different purposes. Accordingly, we design effective mixture-channel purpose routing networks (MCPRNs) with a purpose routing network to detect the purposes of each item and assign them 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.

Original languageEnglish
Title of host publication2019 International Joint Conference on Artificial Intelligence
EditorsSarit Kraus
PublisherInternational Joint Conferences on Artificial Intelligence
Pages3771-3777
Number of pages7
ISBN (Electronic)9780999241141
DOIs
Publication statusPublished - 2019
Event28th International Joint Conference on Artificial Intelligence, IJCAI 2019 - Macao, China
Duration: 10 Aug 201916 Aug 2019

Conference

Conference28th International Joint Conference on Artificial Intelligence, IJCAI 2019
CountryChina
CityMacao
Period10/08/1916/08/19

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Wang, S., Hu, L., Wang, Y., Sheng, Q. Z., Orgun, M., & Cao, L. (2019). Modeling multi-purpose sessions for next-item recommendations via mixture-channel purpose routing networks. In S. Kraus (Ed.), 2019 International Joint Conference on Artificial Intelligence (pp. 3771-3777). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2019/523