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Online Recommender System Based on Social Network Regularization

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Neural Information Processing (ICONIP 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8834))

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Abstract

Although model-based Collaborative Filtering approaches have been widely used in recsys in the past few years. But In practical application, where users’ rating data arrives sequentially and frequently, the model-based approaches have to re-trained completely for new records. For users’ social information has been succeed used in recommendation system in previous work. In this paper, we proposed several online collaborative filtering algorithms using users’ social information to improve the performance of online recommender systems. The algorithms can better use the prior rating and the social network information, which compute fast and scalable in large data. The contribution of this paper are mainly two-fold: (1) We propose an online collaborative filtering algorithm which can better use the social information and prior knowledge; (2) We solve the problem of cold start and users with few ratings.

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Author information

Authors and Affiliations

  1. Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

    Zhuo Wang & Hongtao Lu

Authors
  1. Zhuo Wang
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  2. Hongtao Lu
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Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Faculty of Computer Science and Information Technology Building, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Chu Kiong Loo

  2. Department of Electronics and Communication Engineering,College of Engineering, Jalan IKRAM-UNITEN, Universiti Tenaga Nasional, 43009, Kajang, Selangor, Malaysia

    Keem Siah Yap

  3. School of Engineering and Information Technology, Murdoch University, South St, 6150, Murdoch, Western Australia, Australia

    Kok Wai Wong

  4. Department of Electrical and Electronics Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, 120-749, Seoul, South Korea

    Andrew Teoh

  5. Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Ren’ai Road 111, SIP 215123, Suzhou, Jiangsu Province, China

    Kaizhu Huang

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© 2014 Springer International Publishing Switzerland

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Wang, Z., Lu, H. (2014). Online Recommender System Based on Social Network Regularization. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8834. Springer, Cham. https://doi.org/10.1007/978-3-319-12637-1_61

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  • DOI: https://doi.org/10.1007/978-3-319-12637-1_61

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12636-4

  • Online ISBN: 978-3-319-12637-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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