Recommender systems assist users in quickly finding relevant products based on interests and previous interactions with other users. They benefit companies by encouraging product sales, and they benefit users by minimizing the amount of time spent searching. There are several algorithms in the research for implementing a recommendation system. Collaborative filtering and content-based filtering are two of the most used strategies for recommendation systems. These strategies can be integrated into a hybrid recommendation system to boost efficiency and accuracy. In this study, we used matrix factorization-based models using Surprise which stands for Simple Python Recommendation System Engine, a Python scikit for constructing and explaining recommender system with explicit ratings. We used this by combining methods for building our hybrid approaches. We validated our results with Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Compared to previous works on these datasets we got improved results, and our proposed methods provide less error predictions than other works. Our result, we think, demonstrates that utilizing a hybrid algorithm is superior to using algorithms alone. This research paper proposes and describes a novel hybrid recommender system.