Traditional recommender systems find similarities among users and items to generate recommendations. These systems have various advantages, such as no training process is involved, and they can generate recommendations by simply analyzing the users’ past behaviors. These systems typically list suggestions based on similarity information of users and/or items. However, relying on only user-user or item-item similarity leads to a poor recommendation process, specifically in the case of sparse datasets. Further, the method used for obtaining similarity plays a crucial role in the performance of the recommender systems. Hence, in this study, we have integrated the different similarity measures, such as Pearson correlation and cosine similarity, to generate even better recommendations than the traditional user-user or item-item-based recommendation process. Experimental results obtained using the proposed approach for the benchmark dataset show significant improvement in the recommendation quality.