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TAP: A Two-Level Trust and Personality-Aware Recommender System

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Service-Oriented Computing – ICSOC 2020 Workshops (ICSOC 2020)

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Abstract

Recommender systems (RSs) have been adopted in a variety set of web services to provide a list of items which a user may interact with in near future. Collaborative filtering (CF) is one of the most widely used mechanism in RSs that focuses on preferences of neighbours of similar users. Therefore, it is a critical challenge for CF models to discover a set of appropriate neighbors for a particular user. Most of the current approaches exploit users’ ratings information to find similar users by comparing their rating patterns. However, this may be a simple idea and over-tested by the current studies, which may fail under data sparsity problem. Recommender system as an intelligent system needs to help users with their decision making process, and facilitate them with personalized suggestions. In real world, people are willing to share similar interest with those who have the same personality type; and then among all similar personality users pope may only take advice and recommendation from the trustworthy ones. Therefore, in this paper we propose a two-level model, TAP, which analyzes users’ behaviours to first detect their personality types, and then incorporate trust information to provide more customized recommendations. We mathematically model our approach based on the matrix factorization to consider personality and trust information simultaneously. Experimental results on a real-world dataset demonstrate the effectiveness of our model.

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Notes

  1. 1.

    https://www.netflix.com/.

  2. 2.

    https://www.spotify.com/.

  3. 3.

    https://www.amazon.com/.

  4. 4.

    https://www.ebay.com/.

  5. 5.

    https://www.facebook.com/.

  6. 6.

    https://twitter.com/.

  7. 7.

    https://reviews.history.ac.uk/review/287a.

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Authors and Affiliations

  1. Department of Computing, Macquarie University, Sydney, Australia

    Shahpar Yakhchi, Seyed Mohssen Ghafari & Mehmet Orgun

Authors
  1. Shahpar Yakhchi
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  2. Seyed Mohssen Ghafari
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  3. Mehmet Orgun
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Corresponding author

Correspondence to Shahpar Yakhchi .

Editor information

Editors and Affiliations

  1. Zayed University, Dubai, United Arab Emirates

    Hakim Hacid

  2. Zayed University, Dubai, United Arab Emirates

    Fatma Outay

  3. University of New South Wales, Sydney, NSW, Australia

    Hye-young Paik

  4. Huawei Paris Research Center, Paris, France

    Amira Alloum

  5. National Research Council C.N.R., Pisa, Italy

    Marinella Petrocchi

  6. Deakin University, Waurn Ponds, VIC, Australia

    Mohamed Reda Bouadjenek

  7. Macquarie University, Sydney, NSW, Australia

    Amin Beheshti

  8. Rochester Institute of Technology, Rochester, NY, USA

    Xumin Liu

  9. Université de Paris, Paris, France

    Abderrahmane Maaradji

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Yakhchi, S., Ghafari, S.M., Orgun, M. (2021). TAP: A Two-Level Trust and Personality-Aware Recommender System. In: Hacid, H., et al. Service-Oriented Computing – ICSOC 2020 Workshops. ICSOC 2020. Lecture Notes in Computer Science(), vol 12632. Springer, Cham. https://doi.org/10.1007/978-3-030-76352-7_30

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  • DOI: https://doi.org/10.1007/978-3-030-76352-7_30

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