Web-based services that have become prevalent in people’s everyday life generate huge amounts of data, which makes it hard for the users to search and discover interesting information. Therefore, tools for selecting and delivering personalized contents for users are crucial components of modern web applications. Social recommender systems suggest items to users assuming the knowledge of the users’ social network. This new approach can alleviate the common weaknesses of traditional recommender systems, which completely ignore the users’ personal relationships in the recommendation process. In this paper, a social network based fuzzy recommendation technique is presented, which propagates information through the users’ social network and predicts how users would probably like a certain product in the future. Experimental results on a public dataset show that the proposed method can significantly outperform popular and widely used recommendation system methods in terms of recommendation coverage while maintaining prediction accuracy and performs especially well for cold start users, that have only rated a few items or no item at all previously.