{"type":"ScholarlyArticle","title":"Diversifying Personalized Recommendation with User-session Context","authors":[{"name":"Liang Hu","slug":"liang-hu","given":"Liang","family":"Hu","affiliation":"† University of Technology , Sydney"},{"name":"Longbing Cao","slug":"longbing-cao","given":"Longbing","family":"Cao","affiliation":"† University of Technology , Sydney"},{"name":"Shoujin Wang","slug":"shoujin-wang","given":"Shoujin","family":"Wang","affiliation":"† University of Technology , Sydney"},{"name":"Guandong Xu","slug":"guandong-xu","given":"Guandong","family":"Xu","affiliation":"† University of Technology , Sydney"},{"name":"Jian Cao","slug":"jian-cao","given":"Jian","family":"Cao","affiliation":"‡ Shanghai Jiao Tong University"},{"name":"Zhiping Gu","slug":"zhiping-gu","given":"Zhiping","family":"Gu","affiliation":"* Shanghai Technical Institute of Electronics & Information"}],"abstract":"Recommender systems (RS) have become an integral part of our daily life. However, most current RS often repeatedly recommend items to users with similar profiles. We argue that recommendation should be diversified by leveraging session contexts with personalized user profiles. For this, current session-based RS (SBRS) often assume a rigidly ordered sequence over data which does not fit in many real-world cases. Moreover, personalization is often omitted in current SBRS. Accordingly, a personalized SBRS over relaxedly ordered user-session contexts is more pragmatic. In doing so, deep-structured models tend to be too complex to serve for online SBRS owing to the large number of users and items. Therefore, we design an efficient SBRS with shallow wide-in-wide-out networks, inspired by the successful experience in modern language modelings. The experiments on a real-world e-commerce dataset show the superiority of our model over the state-of-the-art methods.","doi":"10.24963/ijcai.2017/258","url":"https://ijcai.org/proceedings/2017/258","pdf":"https://ijcai.org/proceedings/2017/0258.pdf","markdown":"https://ijcai.org/proceedings/2017/258.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/258","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning A-R","track":"Main track","number":258,"pages":{"first":1858,"last":1864},"keywords":["Natural Language Processing: Natural Language Processing","Knowledge Representation, Reasoning, and Logic: Preference modelling and preference-based reasoning","Machine Learning: Learning Preferences or Rankings","Machine Learning: Neural Networks"],"videos":[],"references":[{"position":1,"raw":"[Baltrunas and Amatriain, 2009] Linas Baltrunas and Xavier Am- atriain. 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