Implicit vs. Explicit Trust in Social Matrix Factorization

Soude Fazeli, Babak Loni, Alejandro Bellogin, Hendrik Drachsler, Peter Sloep

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review


    Incorporating social trust in Matrix Factorization (MF) methods demonstrably improves accuracy of rating prediction. Such approaches mainly use the trust scores explicitly expressed by users. However, it is often challenging to have users provide explicit trust scores of each other. There exist quite a few works, which propose Trust Metrics (TM) to compute and predict trust scores between users based on their interactions. In this paper, we first evaluate several TMs to find out which one can best predict trust scores compared to the actual trust scores explicitly expressed by users. And, second, we propose to incorporate these trust scores inferred from the candidate TMs into social matrix factorization (MF). We investigate if incorporating the implicit trust scores in MF can make rating prediction as accurate as the MF on explicit trust scores. The reported results support the idea of employing implicit trust into MF whenever explicit trust is not available, since the performance of both models is similar.
    Original languageEnglish
    Title of host publicationRecSys'14- Proceedings of the 8th ACM Conference on Recommender systems
    Place of PublicationNew York, NY, USA
    PublisherAssociation for Computing Machinery (ACM)
    Number of pages4
    ISBN (Print)978-1-4503-2668-1
    Publication statusPublished - 6 Oct 2014
    Event8th ACM Conference on Recommender Systems (RecSys'14) - Crowne Plaza Hotel, Foster City, Silicon Valley, United States
    Duration: 6 Oct 201410 Oct 2014


    Conference8th ACM Conference on Recommender Systems (RecSys'14)
    Abbreviated titleRecSys '14
    Country/TerritoryUnited States
    CityFoster City, Silicon Valley
    Internet address


    • recommender system
    • matrix factorization
    • similarity
    • implicit trust
    • social network
    • rating


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