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Trustworthy Collaborative Filtering through Downweighting Noise and Redundancy

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Book cover Web Technologies and Applications (APWeb 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9313))

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Abstract

Proliferation of Electronic Commerce (EC) has revolutionized the way people purchase online. Web-based technologies enable people to more actively interact with merchants and service providers. Such purchasing logs and comments further lead to proliferation of recommender systems. Existing recommendation algorithms exploit either prior transactions or customer reviews to predict user interests towards certain items. Vast noise may be introduced into such information by fake raters, and information redundancy also makes recommender system entangled. In this work, we first examine user reviews and prior transactions to estimate user credibility and item importance to reduce effect from content polluters. Then we propose to alleviate the redundant information from homogeneous users based on link analysis. A unified framework is finally proposed to incorporate them in a mathematical formulation, which can be efficiently optimized. Experimental results on real world data reveal that our model can significantly outperform other baselines.

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Author information

Authors and Affiliations

  1. Institute of Software, School of EECS, Peking University, Beijing, China

    Qiuxiang Dong, Zhi Guan & Zhong Chen

  2. MoE Key Lab of High Confidence Software Technologies (PKU), Peking University, Beijing, China

    Qiuxiang Dong, Zhi Guan & Zhong Chen

  3. MoE Key Lab of Network and Software Security Assurance (PKU), Peking University, Beijing, China

    Qiuxiang Dong, Zhi Guan & Zhong Chen

Authors
  1. Qiuxiang Dong
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  2. Zhi Guan
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  3. Zhong Chen
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Editor information

Editors and Affiliations

  1. University of Hong Kong, Hong Kong, China

    Reynold Cheng

  2. Computer Science, Peking University, Beijing, China

    Bin Cui

  3. Advanced Digital Sciences Center (ADSC), Singapore, Singapore

    Zhenjie Zhang

  4. University of Technology, Guangzhou, China

    Ruichu Cai

  5. Guangxi University, Guangxi, China

    Jia Xu

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© 2015 Springer International Publishing Switzerland

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Dong, Q., Guan, Z., Chen, Z. (2015). Trustworthy Collaborative Filtering through Downweighting Noise and Redundancy. In: Cheng, R., Cui, B., Zhang, Z., Cai, R., Xu, J. (eds) Web Technologies and Applications. APWeb 2015. Lecture Notes in Computer Science(), vol 9313. Springer, Cham. https://doi.org/10.1007/978-3-319-25255-1_11

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  • DOI: https://doi.org/10.1007/978-3-319-25255-1_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25254-4

  • Online ISBN: 978-3-319-25255-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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