{"type":"ScholarlyArticle","title":"BPAM: Recommendation Based on BP Neural Network with Attention Mechanism","authors":[{"name":"Wu-Dong Xi","slug":"wu-dong-xi","given":"Wu-Dong","family":"Xi","affiliation":"School of Data and Computer Science , Sun Yat-sen University , Guangzhou , 510006 , China; Guangdong Province Key Laboratory of Computational Science , Guangzhou , 510275 , China"},{"name":"Ling Huang","slug":"ling-huang","given":"Ling","family":"Huang","affiliation":"School of Data and Computer Science , Sun Yat-sen University , Guangzhou , 510006 , China; Guangdong Province Key Laboratory of Computational Science , Guangzhou , 510275 , China"},{"name":"Chang-Dong Wang","slug":"chang-dong-wang","given":"Chang-Dong","family":"Wang","affiliation":"School of Data and Computer Science , Sun Yat-sen University , Guangzhou , 510006 , China; Guangdong Province Key Laboratory of Computational Science , Guangzhou , 510275 , China"},{"name":"Yin-Yu Zheng","slug":"yin-yu-zheng","given":"Yin-Yu","family":"Zheng","affiliation":"School of Data and Computer Science , Sun Yat-sen University , Guangzhou , 510006 , China"},{"name":"Jianhuang Lai","slug":"jianhuang-lai","given":"Jianhuang","family":"Lai","affiliation":"School of Data and Computer Science , Sun Yat-sen University , Guangzhou , 510006 , China"}],"abstract":"Inspired by the significant success of deep learning, some attempts have been made to introduce deep neural networks (DNNs) in recommendation systems to learn users' preferences for items. Since DNNs are well suitable for representation learning, they enable recommendation systems to generate more accurate prediction. However, they inevitably result in high computational and storage costs. Worse still, due to the relatively small number of ratings that can be fed into DNNs, they may easily lead to over-fitting. To tackle these problems, we propose a novel recommendation algorithm based on Back Propagation (BP) neural network with Attention Mechanism (BPAM). In particular, the BP neural network is utilized to learn the complex relationship of the target users and their neighbors. Compared with deep neural network, the shallow neural network, i.e., BP neural network, can not only reduce the computational and storage costs, but also prevent the model from over-fitting. In addition, an attention mechanism is designed to capture the global impact on all nearest target users for each user.  Extensive experiments on eight benchmark datasets have been conducted to evaluate the effectiveness of the proposed model.","doi":"10.24963/ijcai.2019/542","url":"https://ijcai.org/proceedings/2019/542","pdf":"https://ijcai.org/proceedings/2019/0542.pdf","markdown":"https://ijcai.org/proceedings/2019/542.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/542","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":542,"pages":{"first":3905,"last":3911},"keywords":["Machine Learning: Recommender Systems","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Bell and Koren, 2007] Robert M. 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