Abstract—Most models of automated web recommender systems depend on data mining algorithms to discover useful navigational patterns from the user’s previous browsing history. This paper presents a new model for developing a collaborative web recommendation system using a new technique for knowledge extraction. The proposed model introduces two techniques: cluster similarity-based technique and rule extraction technique to provide proper recommendations that meet the user’s needs. A cluster similarity-based technique groups the sessions that share common interests and behaviors according to a new similarity measure between the web users’ sessions. The rule extraction technique, which is based on a trained Artificial Neural Network (ANN) using a Genetic Algorithm (GA), is performed to discover groups of accurate and comprehensible rules from the clustering sessions. For extracting rules that belong to a specific cluster, GA can be applied to get the perfect values of the pages that maximize the output function of this cluster. A set of pruning schemes is proposed to decrease the size of the rule set and remove non-interesting rules. The resulting set of web pages recommended for a specific cluster is the dominant page in all rules that belong to this cluster. The experimental results indicate the proposed model’s efficiency in improving the classification’s precision and recall.
 
Keywords—collaborative web recommender systems, web usage mining, web log file, Artificial Neural Network (ANN), Neural Network (NN), Genetic Algorithm (GA), clustering technique, knowledge extraction technique


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