Using association analysis of web data in recommender systems

The numerous web sites existing nowadays make available more information than a user can manage. Thus, an essential requirement of current web applications is to provide users with instruments for personalized selective retrieval of web information. In this paper, a procedure for making personalized recommendations is proposed. The method is based on building a predictive model from an association model of Web data. It uses a set of association rules generated by a data mining algorithm that discovers knowledge in an incremental way. These rules provide models with relevant patterns that minimize the recommendation errors.