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International Conference on Knowledge Science, Engineering and Management

KSEM 2011: Knowledge Science, Engineering and Management pp 56–67Cite as

Gaussian Process for Recommender Systems

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 7091)

Abstract

Nowadays, recommender systems are becoming increasingly important because they can filter noisy information and predict users’ preferences. As a result, recommender system has become one of the key technologies for the emerging personalized information services. To these services, when making recommendations, the items’ qualities, items’ correlation, and users’ preferences are all important factors to consider. However, traditional memory-based recommender systems, including the widely used user-oriented and item-oriented collaborative filtering methods, can not take all these information into account. Meanwhile, the model-based methods are often too complex to implement. To that end, in this paper we propose a Gaussian process based recommendation model, which can aggregate all of above factors into a unified system to make more appropriate and accurate recommendations. This model has a solid statistical foundation and is easy to implement. Furthermore, it has few tunable parameters, therefore it is very suitable for a baseline algorithm. The experimental results on the MovieLens data set demonstrate the effectiveness of our method, and it outperforms several state-of-the-art algorithms.

Keywords

  • Gaussian Process
  • Recommender Systems
  • Collaborative Filtering

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  • DOI: 10.1007/978-3-642-25975-3_6
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Author information

Authors and Affiliations

  1. University of Science and Technology of China, Hefei, Anhui, China

    Qi Liu, Enhong Chen, Biao Xiang & Liang He

  2. University of Texas, Arlington, TX, 76019, USA

    Chris H. Q. Ding

Authors
  1. Qi Liu
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  2. Enhong Chen
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  3. Biao Xiang
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  4. Chris H. Q. Ding
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  5. Liang He
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Editor information

Editors and Affiliations

  1. Management Science and Information Systems Department, Rutgers, the State University of New Jersey, 1, Washington Park, 07102, Newark, NJ, USA

    Hui Xiong

  2. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China

    W. B. Lee

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Liu, Q., Chen, E., Xiang, B., Ding, C.H.Q., He, L. (2011). Gaussian Process for Recommender Systems. In: Xiong, H., Lee, W.B. (eds) Knowledge Science, Engineering and Management. KSEM 2011. Lecture Notes in Computer Science(), vol 7091. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25975-3_6

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  • DOI: https://doi.org/10.1007/978-3-642-25975-3_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25974-6

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