A Recommendation Algorithm Considering User Trust and Interest

  • Chuanmin MiEmail author
  • Peng Peng
  • Rafał Mierzwiak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10842)


A traditional collaborative filtering recommendation algorithm has problems with data sparseness, a cold start and new users. With the rapid development of social network and e-commerce, building the trust between users and user interest tags to provide a personalized recommendation is becoming an important research issue. In this study, we propose a probability matrix factorization model (STUIPMF) by integrating social trust and user interest. First, we identified implicit trust relationship between users and potential interest label from the perspective of user rating. Then, we used a probability matrix factorization model to conduct matrix decomposition of user ratings information, user trust relationship, and user interest label information, and further determined the user characteristics to ease data sparseness. Finally, we used an experiment based on the Epinions website’s dataset to verify our proposed method. The results show that the proposed method can improve the recommendation’s accuracy to some extent, ease a cold start and solve new user problems. Meanwhile, the STUIPMF approach, we propose, also has a good scalability.


Data mining Recommender system Collaborative filtering Social trust Interest tag Probability matrix factorization 


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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Nanjing University of Aeronautics and AstronauticsNanjingPeople’s Republic of China
  2. 2.Poznan University of TechnologyPoznanPoland

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