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A Collaborative Filtering Recommender Approach by Investigating Interactions of Interest and Trust

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Knowledge Engineering and Management

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 214))

Abstract

Collaborative filtering-based recommenders operate on the assumption that similar users share similar tastes; however, due to data sparsity of the input ratings matrix, traditional collaborative filtering methods suffer from low accuracy because of the difficulty in finding similar users and the lack of knowledge about the preference of new users. This paper proposes a recommender system based on interest and trust to provide an enhanced recommendations quality. The proposed method incorporates trust derived from both explicit and implicit feedback data to solve the problem of data sparsity. New users can highly benefit from aggregated trust and interest in the form of reputation and popularity of a user as a recommender. The performance is evaluated using two datasets of different sparsity levels, viz. Jester dataset and MovieLens dataset, and are compared with traditional collaborative filtering-based approaches for generating recommendations.

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Acknowledgments

This work is supported in part by the National Key Technology R&D Program (No. 2012BAH16F02), the Natural Science Foundation of China (Grant No. 61003254 and No. 60903038), and the Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China

    Surong Yan

  2. College of Information, Zhejiang University of Finance and Economics, Hangzhou, 310018, China

    Surong Yan

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  1. Surong Yan
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Correspondence to Surong Yan .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, China, People's Republic

    Fuchun Sun

  2. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China, People's Republic

    Tianrui Li

  3. Department of Computer Science and Techn, Tsinghua University, Beijing, China, People's Republic

    Hongbo Li

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Yan, S. (2014). A Collaborative Filtering Recommender Approach by Investigating Interactions of Interest and Trust. In: Sun, F., Li, T., Li, H. (eds) Knowledge Engineering and Management. Advances in Intelligent Systems and Computing, vol 214. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37832-4_16

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  • DOI: https://doi.org/10.1007/978-3-642-37832-4_16

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37831-7

  • Online ISBN: 978-3-642-37832-4

  • eBook Packages: EngineeringEngineering (R0)

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