One of the most famous methods for recommendation is user-based Collaborative Filtering (CF). This system compares active user’s items rating with historical rating records of other users to find similar users and recommending items which seems interesting to these similar users and have not been rated by the active user. As a way of computing recommendations, the ultimate goal of the user-based collaborative filtering is recommending items with the high accuracy and coverage degree. Nevertheless, some famous limitations are obstacles to meet them. They are Scalability, Sparseness and new item problems. Scalability problem can be handled with the use of Data Mining techniques like clustering. However, use of this technique often leads to the lower recommendation accuracy. Nevertheless, two other problems still remain. Involving Semantic knowledge can increase the performance of recommendation in sparseness and New-Item Problem conditions as well. This paper presents a new approach to deal with the drawbacks of user-based CF systems for web pages recommendation by Combination of Semantic Knowledge with Web Usage Mining (WUM). Semantic knowledge of web pages are extracted and subsequently incorporated into the navigation patterns of each cluster which obtained from clustering the access sessions to get the Semantic Patterns of each cluster. The cluster with the most relevant semantic pattern is chosen with the comparison of semantic representation of the active user session with the semantic patterns and the proper web pages are recommended based on a switching recommendation engine. This engine recommends a list of appropriate recommendations. Results of the implementation of this hybrid web recommender system indicates that this combined approach yields better results in both accuracy and coverage metrics and also has a considerable capability to handle collaborative filtering recommender system for its typical shortcomings .