Recommender systems based on multiple social networks correlation

Abstract

As development of social networks, social recommendation method, an effective information filtering technology, based on sociology rule and network theory, has improved performance of recommendation system and cold start problem. For data deep fusion and diverse development of social platform, the social relationship between users becomes more and more complex. The complexity of multiple social networks challenges social recommendation. However, most existing social recommendation methods focus on single social network, and multi-layer recommendation methods ignore nonlinearity and coupling between different social relationships. To tackle these problems, we propose a probabilistic matrix factorization model for multiply social networks joint recommendation framework based on joint probability distribution. This model analyzes different types of classic social networks and distribution function of user preferences similarity. Then we present unified model of recommendation based on social networks, as well as extensible multiply social networks joint recommendation method. The experimental results demonstrate comparing with relevant social recommendation algorithms; our method performs better on some evaluation indexes such as accuracy and errors.