Recommender systems (RS) are information filtering systems that attempt to predict a user’s rating or preference for an item. It addresses the issue of information overload by providing users with personalized recommendations. RS are typically used for individual recommendations, such as movies, music, and tourists. The tendency-based recommendation technique is easier to use, more convenient, and more precise when compared to the currently available traditional collaborative filtering (CF) techniques. It predicts the final ratings of unrated items using item and user tendency. The personalized tendency approach (PTA) makes recommendations more user-centric using different correlation techniques. We propose two different hybrid approaches to predict the ratings of unrated items. Hybrid approaches are the combinations of Entropy and Clarity-based approach. The recommendations were not user-centric, using only the hybrid approach. This paper merges PTA with the hybrid approach using NHSM similarity. Further, we find a trustee user for an active user. Moreover, we propose a trust-based similarity prediction method. Experimental results show that the proposed approach outperforms the existing methods.