An online social network (OSN) is crowded with people and their huge number of post and hence filtering truthful content and/or filtering truthful content creator is a great challenge. The online recommender system helps to get such information from OSN and suggest the valuable item or user. But in reality people have more belief on recommendation from the people they trust than from untrusted sources. Getting recommendation from the trusted people derived from social network is called Trust-Enhanced Recommender System (TERS). A Trust-Boosted Recommender System (TBRS) is proposed in this paper to address the challenge in identifying trusted users from social network. The proposed recommender system is a fuzzy multi attribute recommender system using boosted vector similarity measure designed to predict trusted users from social networks with reduced error. Performance analysis of the proposed model in terms of accuracy measures such as precision@k and recall@k and error measures, namely, MAE, MSE and RMSE is discussed in this paper. The evaluation shows that the proposed system outperforms other recommender system with minimum MAE and RMSE.