@inproceedings{a3dcaedaae3e4b7ea637ce087bf1ba06,
title = "Personalized recommendation by exploring social users' behaviors",
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.",
keywords = "entropy, rating behaviors, recommender system, social networks",
author = "Guoshuai Zhao and Xueming Qian and He Feng",
year = "2014",
doi = "10.1007/978-3-319-04117-9\_17",
language = "英语",
isbn = "9783319041162",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 2",
pages = "181--191",
booktitle = "MultiMedia Modeling - 20th Anniversary International Conference, MMM 2014, Proceedings",
edition = "PART 2",
note = "20th Anniversary International Conference on MultiMedia Modeling, MMM 2014 ; Conference date: 06-01-2014 Through 10-01-2014",
}