Collaborative Web Recommendation Systems Based on an Effective Fuzzy Association Rule Mining Algorithm (FARM)

Web-based product and recommendation systems have become ever popular on-line business practice with increasing emphasis on modeling customer needs and providing them with targeted or personalized service solutions in real-time interaction. Recommender systems is a specific type of information filtering system technique that attempts to recommend information items like images, web pages, etc that are likely to be of interest to the user. Normally, a recommender system compares a user profile to some reference characteristics, and seeks to predict the 'rating' and retrieve the query elements. This system can be classified into two groups: one is Content-based recommendation and another is collaborative recommendation system. Content based recommendation tries to recommend web sites similar to those web sites the user has liked, whereas collaborative recommendation tries to find some users who share similar tastes with the given user and recommends web sites they like to that user. Based on web usage data in adoptive association rule based web mining the association rules were applied to personalization. The technique makes use of apriori algorithm to generate association rules. Even this method has some disadvantages. An effective Fuzzy Association Rule Mining Algorithm (FARM) is proposed by the author to overcome those disadvantages. This proposed Fuzzy HARM algorithm for association rule mining in web recommendation system results in better quality and performance.