Applying and comparing hidden Markov model and fuzzy clustering algorithms to Web usage data for recommender systems

In this study, we apply and compare some of the methods of usage pattern discovery, like simple k-means clustering algorithm, fuzzy relational subtractive clustering algorithm, fuzzy mean field annealing (MFA) clustering and Hidden Markov Model (HMM), for recommender systems. We use metrics like prediction strength, hit ratio, precision, prediction ability and F-Score to compare the applied methods on the Web usage data. Fuzzy MFA and HMM acted better than other methods due to fuzzy nation of human behavior in navigation and extra information utilized in sequence analysis.