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ISoTrustSeq: a social recommender system based on implicit interest, trust and sequential behaviors of users using matrix factorization

  • Vahideh Nobahari
  • Mehrdad Jalali
  • Seyyed Javad Seyyed Mahdavi
Article
  • 27 Downloads

Abstract

Recommender systems try to propose a list of main interests of an on line social network user based on his predicted rating values. In the recent years, several methods are proposed such as Interest Social Recommendation method (ISoRec), and Social Recommendation method based on trust Sequence Matrix Factorization which employs matrix factorization techniques to address the trust propagation and sequential behaviors issues. Main drawback of these works is that they ignore implicit interest of users. Therefore, the main goal of this paper is to solve user-item rating based on the trust, sequential interest and the implicit interest of users, simultaneously. In order to solve this problem, our proposed method combines these parameters as its inputs. This method based on matrix factorization named as ISoTrustSeq. Experimental results show higher accuracy of predicted values in compared to the above-mentioned methods. Our results are also much better than these methods in terms of variation in the number of user-items features.

Keywords

Online social networks Recommender system Matrix factorizations Trust Sequential behaviors Implicit interest 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Vahideh Nobahari
    • 1
  • Mehrdad Jalali
    • 1
  • Seyyed Javad Seyyed Mahdavi
    • 1
  1. 1.Department of Computer, Mashhad BranchIslamic Azad UniversityMashhadIran

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