{"type":"ScholarlyArticle","title":"Memory Augmented Neural Model for Incremental Session-based Recommendation","authors":[{"name":"Fei Mi","slug":"fei-mi","given":"Fei","family":"Mi","affiliation":"Artificial Intelligence Laboratory , École Polytechnique Fédérale de Lausanne (EPFL)"},{"name":"Boi Faltings","slug":"boi-faltings","given":"Boi","family":"Faltings","affiliation":"Artificial Intelligence Laboratory , École Polytechnique Fédérale de Lausanne (EPFL)"}],"abstract":"Increasing concerns with privacy have stimulated interests in Session-based Recommendation (SR) using no personal data other than what is observed in the current browser session. Existing methods are evaluated in static settings which rarely occur in real-world applications. To better address the dynamic nature of SR tasks, we study an incremental SR scenario, where new items and preferences appear continuously. We show that existing neural recommenders can be used in incremental SR scenarios with small incremental updates to alleviate computation overhead and catastrophic forgetting. More importantly, we propose a general framework called Memory Augmented Neural model (MAN). MAN augments a base neural recommender with a continuously queried and updated nonparametric memory, and the predictions from the neural and the memory components are combined through another lightweight gating network. We empirically show that MAN is well-suited for the incremental SR task, and it consistently outperforms state-oft-he-art neural and nonparametric methods. We analyze the results and demonstrate that it is particularly good at incrementally learning preferences on new and infrequent items.","doi":"10.24963/ijcai.2020/300","url":"https://ijcai.org/proceedings/2020/300","pdf":"https://ijcai.org/proceedings/2020/0300.pdf","markdown":"https://ijcai.org/proceedings/2020/300.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/300","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Machine Learning","track":"Main track","number":300,"pages":{"first":2169,"last":2176},"keywords":["Machine Learning: Online Learning","Machine Learning: Time-series;Data Streams","Machine Learning: Recommender Systems"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25121/file_25121.m3u8","length_sec":288,"provider":"hls"}],"references":[{"position":1,"raw":"[Bakhtin et al., 2018] Anton Bakhtin, Arthur Szlam, Marc'Aurelio Ranzato, and Edouard Grave. 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