Due to the information overload on the World Wide Web, the user suffers from difficulty in selecting items. Social cataloging services allow users to use products or services and share their opinions and experiences, which are effective not only for themselves, but also for other users. Considering user behavior and product features as the two determining factors, recommender systems have significantly influenced the item selection process. In this paper, according to the emotions reflected in the user Tags, a Tag-based recommendation method is proposed. The method works in the following way: information related to these emotions, along with other information received from the user as well as the content information of the items results in obtaining the degree of similarity between them. This process ultimately helps to improve the performance of the recommender systems. Testing the abovementioned process on a real database, namely Movielense, showed that the proposed method performed better than previous ones and has reduced errors and increased accuracy in predicting ratings.