{"type":"ScholarlyArticle","title":"CFM: Convolutional Factorization Machines for Context-Aware Recommendation","authors":[{"name":"Xin Xin","slug":"xin-xin","given":"Xin","family":"Xin","affiliation":"University of Glasgow"},{"name":"Bo Chen","slug":"bo-chen","given":"Bo","family":"Chen","affiliation":"Shanghai Jiao Tong University"},{"name":"Xiangnan He","slug":"xiangnan-he","given":"Xiangnan","family":"He","affiliation":"University of Science and Technology of China"},{"name":"Dong Wang","slug":"dong-wang","given":"Dong","family":"Wang","affiliation":"Shanghai Jiao Tong University"},{"name":"Yue Ding","slug":"yue-ding","given":"Yue","family":"Ding","affiliation":"Shanghai Jiao Tong University"},{"name":"Joemon Jose","slug":"joemon-jose","given":"Joemon","family":"Jose","affiliation":"University of Glasgow"}],"abstract":"Factorization Machine (FM) is an effective solution for context-aware recommender systems (CARS) which models second-order feature interactions by inner product. However, it is insufficient to capture high-order and nonlinear interaction signals. While several recent efforts have enhanced FM with neural networks, they assume the embedding dimensions are independent from each other and model high-order interactions in a rather implicit manner. In this paper, we propose Convolutional Factorization Machine (CFM) to address above limitations. Specifically, CFM models second-order interactions with outer product, resulting in ''images'' which capture correlations between embedding dimensions. Then all generated ''images'' are stacked, forming an interaction cube. 3D convolution is applied above it to learn high-order interaction signals in an explicit approach. Besides, we also leverage a self-attention mechanism to perform the pooling of features to reduce time complexity. We conduct extensive experiments on three real-world datasets, demonstrating significant improvement of CFM over competing methods for context-aware top-k recommendation.","doi":"10.24963/ijcai.2019/545","url":"https://ijcai.org/proceedings/2019/545","pdf":"https://ijcai.org/proceedings/2019/0545.pdf","markdown":"https://ijcai.org/proceedings/2019/545.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/545","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":545,"pages":{"first":3926,"last":3932},"keywords":["Multidisciplinary Topics and Applications: Recommender Systems","Machine Learning: Recommender Systems","Machine Learning: Learning Preferences or Rankings"],"videos":[],"references":[{"position":1,"raw":"[Baltrunas et al., 2015] Linas Baltrunas, Karen Church, Alexandros Karatzoglou, and Nuria Oliver. 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