  Due the success of emerging Web 2.0, and different social network Web sites
such as Amazon and movie lens, recommender systems are creating unprecedented
opportunities to help people browsing the web when looking for relevant
information, and making choices. Generally, these recommender systems are
classified in three categories: content based, collaborative filtering, and
hybrid based recommendation systems. Usually, these systems employ standard
recommendation methods such as artificial neural networks, nearest neighbor, or
Bayesian networks. However, these approaches are limited compared to methods
based on web applications, such as social networks or semantic web. In this
paper, we propose a novel approach for recommendation systems called semantic
social recommendation systems that enhance the analysis of social networks
exploiting the power of semantic social network analysis. Experiments on
real-world data from Amazon examine the quality of our recommendation method as
well as the performance of our recommendation algorithms.
