Web recommendation systems are ubiquitous in the world used to overcome the product overload on e-commerce websites. Among various filtering algorithms, Collaborative Filtering and Content Based Filtering are the best recommendation approaches. Being popular, these filtering approaches still suffer from various limitations such as Cold Start Problem, Sparsity and Scalability all of which lead to poor recommendations. In this paper, we propose a hybrid system-based book recommendation system that anticipates recommendations. The proposed system is a mixture of collaborative filtering and content based filtering which can be explained in three phases: In the first phase, it identifies the users who are analogous to the active user by matching users' profiles. In the second phase, it chooses the candidate's item for every similar user by obtaining vectors V[subscript c] and V[subscript m] corresponding to the user's profile and the item contents. After calculating the prediction value for each item using the Resnick prediction equation, items are suggested to the target user in the final phase. We compared our proposed system to current state-of-the-art recommendation models, such as collaborative filtering and content-based filtering. It is shown in the experimental section that the proposed hybrid filtering approach outperforms conventional collaborative filtering and content-based filtering.