Combining Web Usage Mining and Fuzzy Inference for Website Personalization

Personalization tailors a user’s interaction with the Web information space based on information gathered about them. Declarative user information such as manually entered profiles continue to raise privacy concerns and are neither scalable nor flexible in the face of very active dynamic Web sites and changing user trends and interests. One way to deal with this problem is through a complete automated Web personalization system. Such a system can be based on Web usage mining to discover Web usage profiles, followed by a recommendation system that can respond to the users’ individual interests. Significant amounts of error and uncertainty can permeate all the stages of Web personalization. Therefore, we present a fast and intuitive approach to provide Web recommendations using a fuzzy inference engine with rules that are automatically derived from prediscovered user profiles. We perform extensive simulations with real data to study the effect of different parametrization options, and to empirically compare the performance of the proposed approach with that of collaborative filtering and nearest-profile based recommendation strategies. This paper’s main contributions are: The proposal of a profile based fuzzy recommendation engine with extensive empirical comparison of different fuzzy input membership derivation and parametrization options, and comparison with approaches based on collaborative filtering and nearest profile recommendations. The proposed fuzzy recommendation method achieves high coverage compared to K-NN and nearest-profile recommendations despite slightly lower precision. Finally, we note that fuzzy recommendations are very intuitive, deal with natural overlap in user interests, and are very low in cost compared to collaborative filtering: They are extremely faster and require much lower main memory at recommendation time (no need to store or compare to a large number of instances). This makes fuzzy recommendations suitable for real time recommendations in a live setting on today's most active and huge websites.