{"type":"ScholarlyArticle","title":"RecoNet: An Interpretable Neural Architecture for Recommender Systems","authors":[{"name":"Francesco Fusco","slug":"francesco-fusco","given":"Francesco","family":"Fusco","affiliation":"IBM Research AI; University of Lausanne , Department of Business and Economics."},{"name":"Michalis Vlachos","slug":"michalis-vlachos","given":"Michalis","family":"Vlachos","affiliation":"IBM Research AI; University of Lausanne"},{"name":"Vasileios Vasileiadis","slug":"vasileios-vasileiadis","given":"Vasileios","family":"Vasileiadis","affiliation":"ipQuants AG"},{"name":"Kathrin Wardatzky","slug":"kathrin-wardatzky","given":"Kathrin","family":"Wardatzky","affiliation":"IBM Research AI"},{"name":"Johannes Schneider","slug":"johannes-schneider","given":"Johannes","family":"Schneider","affiliation":"University of Liechtenstein"}],"abstract":"Neural systems offer high predictive accuracy but are plagued by long training times and low interpretability. We present a simple neural architecture for recommender systems that lifts several of these shortcomings. Firstly, the approach has a high predictive power that is comparable to state-of-the-art recommender approaches. Secondly, owing to its simplicity, the trained model can be interpreted easily because it provides the individual contribution of each input feature to the decision. Our method is three orders of magnitude faster than general-purpose explanatory approaches, such as LIME. Finally, thanks to its design, our architecture addresses cold-start issues, and therefore the model does not require retraining in the presence of new users.","doi":"10.24963/ijcai.2019/325","url":"https://ijcai.org/proceedings/2019/325","pdf":"https://ijcai.org/proceedings/2019/0325.pdf","markdown":"https://ijcai.org/proceedings/2019/325.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/325","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 A-L","track":"Main track","number":325,"pages":{"first":2343,"last":2349},"keywords":["Machine Learning: Interpretability","Machine Learning: Recommender Systems","Machine Learning: Data Mining","Machine Learning: Explainable Machine Learning"],"videos":[],"references":[{"position":1,"raw":"'Pulp Fiction (1994)'","title":"Pulp Fiction","authors":null,"year":"1994","venue":"Pulp Fiction","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"'Usual Suspects, The (1995)'","title":"Usual Suspects","authors":null,"year":"1995","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"'Sixth Sense, The (1999)'","title":"Sixth Sense","authors":null,"year":"1999","venue":"The","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"'Few Good Men, A (1992)'","title":"Few Good Men, A","authors":null,"year":"1992","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"'Full Monty, The (1997)' 6. 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