Many efforts have been done to tackle the problem of information abundance in the World Wide Web. Growth in the number of web users and the necessity of making the information available on the web make web recommender systems very critical and popular. Recommender systems use the knowledge obtained through the analysis of users' navigational behavior to customize a web site to the needs of each particular user or set of users. Most of the existing recommender systems use either content-based or collaborative filtering approach. It is difficult to decide which one of these approaches is the most effective one to be used, as each of them has both strengths and weaknesses. Therefore, a combination of different methods as a hybrid system can overcome these limitations and increase the effectiveness of the system. This paper introduces a new hybrid recommender system by exploiting a combination of collaborative filtering and content-based approaches in a way that resolves the drawbacks of each approach and makes a great improvement in the variety of recommendations in comparison to each individual approach. We introduce a new fuzzy clustering approach based on genetic algorithms and create a two-layer graph. After applying this clustering algorithm to both layers of the graph, we compute the similarity between web pages and users, and propose recommendations using the content-based, collaborative and hybrid approaches. A detailed comparison on all the mentioned approaches shows that the hybrid approach recommends the web pages which haven't been yet viewed by any user, more accurately and precisely than other approaches. Therefore, the evaluation of the results reveals that the novel proposed combination approach achieves more accurate predictions and more appropriate recommendations than each individual approach.