{"type":"ScholarlyArticle","title":"A^3NCF: An Adaptive Aspect Attention Model for Rating Prediction","authors":[{"name":"Zhiyong Cheng","slug":"zhiyong-cheng","given":"Zhiyong","family":"Cheng","affiliation":"School of Computing , National University of Singapore , Singapore"},{"name":"Ying Ding","slug":"ying-ding","given":"Ying","family":"Ding","affiliation":"Vipshop US Inc. , San Jose , CA , USA"},{"name":"Xiangnan He","slug":"xiangnan-he","given":"Xiangnan","family":"He","affiliation":"School of Computing , National University of Singapore , Singapore"},{"name":"Lei Zhu","slug":"lei-zhu","given":"Lei","family":"Zhu","affiliation":"School of Information Science and Engineering , Shandong Normal University , China"},{"name":"Xuemeng Song","slug":"xuemeng-song","given":"Xuemeng","family":"Song","affiliation":"School of Computer Science and Technology , Shandong University , China"},{"name":"Mohan Kankanhalli","slug":"mohan-kankanhalli","given":"Mohan","family":"Kankanhalli","affiliation":"School of Computing , National University of Singapore , Singapore"}],"abstract":"Current recommender systems consider the various aspects of items for making accurate recommendations. Different users place different importance to these aspects which can be thought of as a preference/attention weight vector. Most existing recommender systems assume that for an individual, this vector is the same for all items.  However, this assumption is often invalid, especially when considering a user's interactions with items of diverse characteristics. To tackle this problem, in this paper, we develop a novel aspect-aware recommender model named A$^3$NCF, which can capture the varying aspect attentions that a user pays to different items. Specifically, we design a new topic model to extract user preferences and item characteristics from review texts. They are then used to 1) guide the representation learning of users and items, and 2) capture a user's special attention on each aspect of the targeted item with an attention network.  Through extensive experiments on several large-scale datasets, we demonstrate that our model outperforms the state-of-the-art review-aware recommender systems in the rating prediction task.","doi":"10.24963/ijcai.2018/521","url":"https://ijcai.org/proceedings/2018/521","pdf":"https://ijcai.org/proceedings/2018/0521.pdf","markdown":"https://ijcai.org/proceedings/2018/521.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/521","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Multidisciplinary Topics and Applications","track":"Main track","number":521,"pages":{"first":3748,"last":3754},"keywords":["Knowledge Representation and Reasoning: Preference Modelling and Preference-Based Reasoning","Multidisciplinary Topics and Applications: Recommender Systems"],"videos":[],"references":[{"position":1,"raw":"[Bao et al., 2014] Yang Bao, Hui Fang, and Jie Zhang. 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