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Context-aware recommender system using trust network

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Abstract

Context-Aware Recommender Systems (CARS) improve traditional Recommender Systems (RS) in a wide array of domains and applications. However, CARS suffer from several inherent issues such as data sparsity and cold start. Incorporating trust into recommender systems can handle these issues. Trust-aware recommender systems use information from social networks such as trust statements, which prove another valuable information source. This paper exploits the advantages of these two systems by incorporating both trust and context information. We propose a hybrid approach: Trust based Context aware Post Filtering Approach that uses trust statements as a rich information with context compensation method of contextual post-filtering approach. Our approach utilizes the relative average difference among the context on output of trust aware collaborative filtering by incorporating explicit and implicit trust information. We also use a confidence concept to remove non-confident users from the trust network before generating prediction. The performed experiments show that the proposed approach improves upon the standard RS and outperforms recommendation approaches on real world dataset.

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Authors and Affiliations

  1. LRIT Laborartory, Abou Bekr Belkaid University of Tlemcen, Chetouane, Algeria

    Zeyneb El Yebdri & Fedoua Lahfa

  2. LabRI Laborartory, Ecole Superieure en Informatique, Sidi Bel Abbes, Algeria

    Sidi Mohammed Benslimane

  3. LIRIS Laboratory, Claude Bernard Lyon1 University, Lyon, France

    Mahmoud Barhamgi & Djamal Benslimane

Authors
  1. Zeyneb El Yebdri
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  2. Sidi Mohammed Benslimane
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  3. Fedoua Lahfa
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  4. Mahmoud Barhamgi
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  5. Djamal Benslimane
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Correspondence to Zeyneb El Yebdri.

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El Yebdri, Z., Benslimane, S.M., Lahfa, F. et al. Context-aware recommender system using trust network. Computing 103, 1919–1937 (2021). https://doi.org/10.1007/s00607-020-00876-9

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