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Title:
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APPLYING AND COMPARING HIDDEN MARKOV MODEL AND FUZZY CLUSTERING ALGORITHMS TO WEB USAGE DATA FOR RECOMMENDER SYSTEMS |
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Author(s):
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Shaghayegh Sahebi , Farhad Oroumchian , Ramtin Khosravi |
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ISBN:
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978-972-8924-63-8 |
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Editors:
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Hans Weghorn and Ajith P. Abraham |
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Year:
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2008 |
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Edition:
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Single |
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Keywords:
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Web mining, usage pattern discovery, recommendation system, fuzzy clustering, Hidden Markov Model |
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Type:
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Poster/Demonstration |
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First Page:
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179 |
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Last Page:
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181 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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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. |
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