Abstract
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.
This work was supported by the Project of National Social Science Foundation of China (17BGL055).
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Mi, C., Peng, P., Mierzwiak, R. (2018). A Recommendation Algorithm Considering User Trust and Interest. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10842. Springer, Cham. https://doi.org/10.1007/978-3-319-91262-2_37
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