Collaborative filtering (CF) recommender systems are typically unable to generate adequate recommendations in sparse datasets. Empirical evidence suggests that incorporation of a trust network among the users of a recommender system can significantly help to alleviate this problem. For this reason, some studies have been done on combining CF with trust-enhanced recommender system. In this study, we analyze the switching hybrid recommender system with the CF and trust-enhanced recommender system components from both rating coverage and mean absolute error point of view. Experiments on a dataset from Epinions.com prove that, although the rating coverage of this hybrid method is better than both (CF and trust-enhanced RS), but has lower accuracy than just using trust-enhanced RS. In other words, trust-enhanced RS outperforms the hybrid recommender system consisting of CF and trust-enhanced RS. Finally, we justify this result using analytical method.
