Nowadays, users rely on the web for information gathering. Accordingly, web usage mining becomes one important subject of research. Such research area covers prediction of user near future intentions, web-based personalized services, customer profiling, and adaptive web sites. Web page prediction is strongly limited by the nature of web logs, the intrinsic complexity of the problem and the tight efficiency requirements. This paper proposes a hybrid page ranking model based on web usage mining technique by exploiting session data of users, to enhance the recommendations of the next candidate web page to be accessed. The proposed approach represents a combination between two page ranking approaches. The first one computes the frequency ratio indicating the number of occurrences of each page in the search result. On the other hand, the second approach computes the coverage ratio from similar behavior patterns. As a result of the proposed approach, a set of candidate pages are ranked and the page with highest rate is recommended. The proposed approach has been tested on real data collected and extracted from the web server log file of CTI main web server.