An intelligent recommender system using sequential Web access patterns

To provide intelligent personalized online services such as Web recommendations, it is usually necessary to model users´ Web access behavior. To achieve this, one of the promising approaches is Web usage mining, which mines Web logs for user models and recommendations. Different from most Web recommender systems that are mainly based on clustering and association rule mining, this paper proposes an intelligent Web recommender system known as SWARS (sequential Web access-based recommender system) that uses sequential access pattern mining. In the proposed system, CS-mine, an efficient sequential pattern mining algorithm is used to identify frequent sequential Web access patterns. The access patterns are then stored in a compact tree structure, called Pattern-tree, which is then used for matching and generating Web links for recommendations. In this paper, the proposed SWARS system is described, and its performance is evaluated based on precision, satisfaction and applicability.