Abstract
The World Wide Web is becoming the most important source to search for information or products. But the size and the unstructured nature of the available information makes the location of the right information a challenging task. Recommender systems and web usage mining techniques are two of the main methods used to overcome information overload. In this paper, we present a framework for the next page prediction that exploits users' access history combined with his semantic interests to generate personalized and accurate recommendations. We are suggesting two different approaches for decision fusion between usage and semantic data. The two proposed techniques offered a 47.3% and 54.3% improvement in prediction accuracy over conventional methods for next page prediction. The suggested framework also employs user clustering to focus the search which reduced the prediction time by an average of 68.7% and 63.4%.