Abstract
Recommendation system is a tool that can help users quickly and effectively obtain useful resources in the face of the large amounts of information. Collaborative filtering is a widely used recommendation technology which recommends source for users through similar neighbors’ scores, but is faced with the problem of data sparseness and “cold start”. Although recommendation system based on trust model can solve the above problems to some extent, but still need further improvement to its coverage. To solve these problems, the paper proposes a matrix decomposition algorithm mixed with user trust mechanism (hereinafter referred to as UTMF), The algorithm uses matrix decomposition to fill the score matrix, and combine trust rating information of users in the filling process. According to the results of experiment using the E-opinions Data set, UTMF algorithm can improve the precision of the recommended, effectively ease the cold start problem.
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Zhang, P., Jiang, B. (2016). The Research of Recommendation System Based on User-Trust Mechanism and Matrix Decomposition. In: Carretero, J., et al. Algorithms and Architectures for Parallel Processing. ICA3PP 2016. Lecture Notes in Computer Science(), vol 10049. Springer, Cham. https://doi.org/10.1007/978-3-319-49956-7_8
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DOI: https://doi.org/10.1007/978-3-319-49956-7_8
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