Improving the effectiveness of model based recommender systems for highly sparse and noisy Web usage data

A number of approaches which use model-based collaborative filtering (CF) for scalability in building recommendation systems in Web personalization have poor accuracy due to the fact that Web usage data is often sparse and noisy. Clustering, mining association rules, and sequence pattern discovery have been used to determine the access behavior model. Making use of some of the characteristics of the modeling process can provide significant improvements to recommendation effectiveness. In an earlier work, we introduced a fuzzy hybrid CF technique which inherits the advantages of both memory-based and model-based CF. In this paper, using relational fuzzy subtractive clustering as the first level modeling and then mining association rules within individual clusters, we propose a two level model-based technique, which is scalable and is an enhancement over association rule based recommender systems. Our results from comprehensive experiments using a large real life Web usage data and performance comparisons with memory-based and model-based approaches help substantiate this claim.