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Pacific Rim Knowledge Acquisition Workshop

PKAW 2016: Knowledge Management and Acquisition for Intelligent Systems pp 141–152Cite as

Amalgamating Social Media Data and Movie Recommendation

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9806))

Abstract

Recommender systems (RSs) have become very common recently. However, RS techniques need large amounts of user and product data, which hinders RS usage for businesses with insufficient data. The RS cold-start problem may be mitigated by leveraging external data sources. We demonstrate the feasibility of solving the cold-start problem by implementing a hybrid RS that integrates the Facebook Fan Page data and the genre-classifications data from Yahoo! Movies. Our study amalgamates social media data and machine learning to build a hybrid-filtering RS. We also compared our system with three existing movie RSs—those used by Netflix, YouTube, and Amazon. Within the framework of a hybrid-filtering RS, content-based filtering was used to extract data from Yahoo! Movies and Facebook Fan Pages. The proposed RS overcame the cold-start problem and achieved a satisfactory level of accuracy.

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Acknowledgement

The article is partially supported by MOST-104-2410-H-158-008 and USC-104-05-04001.

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Authors and Affiliations

  1. Department of Information Technology and Management, Shih Chien University, Taipei, Taiwan

    Maria R. Lee & Ying Shun Cai

  2. Graduate Institute of Information Management, National Taipei University, New Taipei City, Taiwan

    Tsung Teng Chen

Authors
  1. Maria R. Lee
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  2. Tsung Teng Chen
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  3. Ying Shun Cai
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Corresponding author

Correspondence to Maria R. Lee .

Editor information

Editors and Affiliations

  1. Tokyo University of Science , Noda, Japan

    Hayato Ohwada

  2. University of Tsukuba, Tokyo, Japan

    Kenichi Yoshida

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Lee, M.R., Chen, T.T., Cai, Y.S. (2016). Amalgamating Social Media Data and Movie Recommendation. In: Ohwada, H., Yoshida, K. (eds) Knowledge Management and Acquisition for Intelligent Systems . PKAW 2016. Lecture Notes in Computer Science(), vol 9806. Springer, Cham. https://doi.org/10.1007/978-3-319-42706-5_11

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  • DOI: https://doi.org/10.1007/978-3-319-42706-5_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42705-8

  • Online ISBN: 978-3-319-42706-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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