Abstract
With the popularity and rapid development of social network, more and more people enjoy sharing their experiences, such as reviews, ratings and moods. And there are great opportunities to solve the cold start and sparse data problem with the new factors of social network like interpersonal influence and interest based on circles of friends. Some algorithm models and social factors have been proposed in this domain, but have not been fully considered. In this paper, two social factors: interpersonal rating behaviors similarity and interpersonal interest similarity, are fused into a consolidated personalized recommendation model based on probabilistic matrix factorization. And the two factors can enhance the inner link between features in the latent space. We implement a series of experiments on Yelp dataset. And experimental results show the outperformance of proposed approach.
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Zhao, G., Qian, X., Feng, H. (2014). Personalized Recommendation by Exploring Social Users’ Behaviors. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O’Connor, N. (eds) MultiMedia Modeling. MMM 2014. Lecture Notes in Computer Science, vol 8326. Springer, Cham. https://doi.org/10.1007/978-3-319-04117-9_17
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DOI: https://doi.org/10.1007/978-3-319-04117-9_17
Publisher Name: Springer, Cham
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