Type of Document Dissertation Author Nkweteyim, Denis Lemongew Author's Email Address nkweteyim@gmail.com URN etd-07212005-054925 Title A COLLABORATIVE FILTERING APPROACH TO PREDICT WEB PAGES OF INTEREST FROM NAVIGATION PATTERNS OF PAST USERS WITHIN AN ACADEMIC WEBSITE Degree Doctor of Philosophy Program Information Science School School of Information Sciences Advisory Committee
Advisor Name Title Professor Stephen C. Hirtle Committee Chair Professor Jerrold H. May Committee Member Professor Michael B. Spring Committee Member Professor Paul Munro Committee Member Professor Peter Brusilovsky Committee Member Keywords
- prediction
- Recommender system
- clustering
- collaborative filtering
- data mining
- association rule mining
- classification
Date of Defense 2005-05-16 Availability unrestricted Abstract This dissertation is a simulation study of factors and techniques involved in designing hyperlink recommender systems that recommend to users, web pages that past users with similar navigation behaviors found interesting. The methodology involves identification of pertinent factors or techniques, and for each one, addresses the following questions: (a) room for improvement; (b) better approach, if any; and (c) performance characteristics of the technique in environments that hyperlink recommender systems operate in. The following four problems are addressed:Web Page Classification. A new metric (PageRank × Inverse Links-to-Word count ratio) is proposed for classifying web pages as content or navigation, to help in the discovery of user navigation behaviors from web user access logs. Results of a small user study suggest that this metric leads to desirable results.
Data Mining. A new apriori algorithm for mining association rules from large databases is proposed. The new algorithm addresses the problem of scaling of the classical apriori algorithm by eliminating an expensive join
step, and applying the apriori property to every row of the database. In this study, association rules show the correlation relationships between user navigation behaviors and web pages they find interesting. The new algorithm has better space complexity than the classical one, and better time efficiency under some conditions
and comparable time efficiency under other conditions.
Prediction Models for User Interests. We demonstrate that association rules that show the correlation relationships between user navigation patterns and web pages they find interesting can be transformed into
collaborative filtering data. We investigate collaborative filtering prediction models based on two approaches for computing prediction scores: using simple averages and weighted averages. Our findings suggest that the
weighted averages scheme more accurately computes predictions of user interests than the simple averages scheme does.
Clustering. Clustering techniques are frequently applied in the design of personalization systems. We studied the performance of the CLARANS clustering algorithm in high dimensional space in relation to the PAM and CLARA clustering algorithms. While CLARA had the best time performance, CLARANS resulted in clusters
with the lowest intra-cluster dissimilarities, and so was most effective in this regard.
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