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In this paper we have presented a Web personalization system based on Web usage mining which can automatically provide effective navigational pointers to a user based on the user's active session and the aggregate usage patterns of other similar users

WebPersonalizer: A Server-Side Recommender System Based on Web Usage Mining

(1991)

Cited by: 35|Views6
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Abstract

Existing approaches to Web personalization often rely heavily on explicit and subjective user input resulting in static profiles which are prone to biases. In this paper we present a usage- based Web personalization system, called WebPersonalizer, drawing heavily upon Web mining techniques, making the personalization process automatic, an...More

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Introduction
  • E-commerce activity that involves the end user is undergoing a significant revolution.
  • The ability to track users’ browsing behavior down to individual mouse clicks has brought the vendor and end customer closer than ever before
  • It is possible for a vendor to personalize the product message for individual customers at a massive scale, a phenomenon that is being referred to as mass customization.
  • This type of personalization is, applicable to any Web browsing activity.
  • User preferences may be obtained explicitly, or by passive observation of users over time as they interact with the system
Highlights
  • E-commerce activity that involves the end user is undergoing a significant revolution
  • In this paper we describe the design and implementation of a usage-based Web personalization system, called WebPersonalizer, which takes into account the full spectrum of Web mining techniques and activities
  • In the rest of this section we present our technique for the derivation of URL clusters, based on clustering user transactions
  • We used the access logs from the Web site of the Association for Consumer Research (ACR) Newsletter for our experiments
  • In this paper we have presented a Web personalization system based on Web usage mining which can automatically provide effective navigational pointers to a user based on the user's active session and the aggregate usage patterns of other similar users
Results
  • The authors used the access logs from the Web site of the Association for Consumer Research (ACR) Newsletter for the experiments.
  • Support filtering was used to eliminate pageviews appearing in less than 0.5% or more than 80% of transactions.
  • For these experiments the authors eliminated short transactions, leaving only transactions with at least 5 references.
  • The total number of remaining pageview URLs in the training and the evaluation sets was 62
Conclusion
  • Conclusions and Future Work

    The Web is providing a direct communication medium between the vendors of products and services, and their clients.
  • The authors plan on conducting experiments with various types of transactions derived from user sessions, for example, to isolate specific types of "content" pages in the recommendation process.
  • The latter task is important in the context of electronic commerce, since the system can automatically guide users to particular product pages based on matching the user's interests with other similar user access patterns
Summary
  • Introduction:

    E-commerce activity that involves the end user is undergoing a significant revolution.
  • The ability to track users’ browsing behavior down to individual mouse clicks has brought the vendor and end customer closer than ever before
  • It is possible for a vendor to personalize the product message for individual customers at a massive scale, a phenomenon that is being referred to as mass customization.
  • This type of personalization is, applicable to any Web browsing activity.
  • User preferences may be obtained explicitly, or by passive observation of users over time as they interact with the system
  • Results:

    The authors used the access logs from the Web site of the Association for Consumer Research (ACR) Newsletter for the experiments.
  • Support filtering was used to eliminate pageviews appearing in less than 0.5% or more than 80% of transactions.
  • For these experiments the authors eliminated short transactions, leaving only transactions with at least 5 references.
  • The total number of remaining pageview URLs in the training and the evaluation sets was 62
  • Conclusion:

    Conclusions and Future Work

    The Web is providing a direct communication medium between the vendors of products and services, and their clients.
  • The authors plan on conducting experiments with various types of transactions derived from user sessions, for example, to isolate specific types of "content" pages in the recommendation process.
  • The latter task is important in the context of electronic commerce, since the system can automatically guide users to particular product pages based on matching the user's interests with other similar user access patterns
Tables
  • Table1: Examples of URL clusters representing aggregate usage profiles
  • Table2: Evaluation scores and the average size of the recommendation set produced by the recommendation engine using a session window size of 2
Download tables as Excel
Funding
  • Support filtering was used to eliminate pageviews appearing in less than 0.5% or more than 80% of transactions (including the site entry page)
Reference
  • R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proceedings of the 20th VLDB conference, pp. 487-499, Santiago, Chile, 1994.
    Google ScholarLocate open access versionFindings
  • R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proceedings of the 20th VLDB conference, pp. 487-499, Santiago, Chile, 1994.
    Google ScholarLocate open access versionFindings
  • A. Buchner and M. D. Mulvenna. Discovering internet marketing intelligence through online analytical Web usage mining. SIGMOD Record, (4) 27, 1999.
    Google ScholarLocate open access versionFindings
  • E. Charniak. Statistical language learning. MIT Press, 1996.
    Google ScholarFindings
  • R. Cooley, B. Mobasher, and J. Srivastava. Data preparation for mining World Wide Web browsing patterns. Journal of Knowledge and Information Systems, (1) 1, 1999.
    Google ScholarLocate open access versionFindings
  • R. Cooley, P-T. Tan., and J. Srivastava. WebSIFT: The Web site information filter system. In Workshop on Web Usage Analysis and User Profiling (WebKKD99), San Diego, August 1999.
    Google ScholarFindings
  • J. Herlocker, J. Konstan, A. Borchers, and J. Riedl. An algorithmic framework for performing collaborative filtering. To appear in Proceedings of the 1999 Conference on Research and Development in Information Retrieval, August 1999.
    Google ScholarLocate open access versionFindings
  • T. Joachims, D. Freitag, and T. Mitchell. Webwatcher: A tour guide for the world wide web. In the 15th International Conference on Artificial Intelligence, Nagoya, Japan, 1997.
    Google ScholarLocate open access versionFindings
  • J. Konstan, B. Miller, D. Maltz, J. Herlocker, L. Gordon, and J. Riedl. GroupLens: applying collaborative filtering to usenet news. Communications of the ACM (40) 3, 1997.
    Google ScholarLocate open access versionFindings
  • H. Lieberman. Letizia: An agent that assists web browsing. In Proc. of the 1995 International Joint Conference on Artificial Intelligence, Montreal, Canada, 1995.
    Google ScholarLocate open access versionFindings
  • B. Mobasher, R. Cooley, and J. Srivastava. Creating adaptive web sites through usage-based clustering of urls. In IEEE Knowledge and Data Engineering Workshop (KDEX'99), 1999.
    Google ScholarLocate open access versionFindings
  • O. Nasraoui, H. Frigui, A. Joshi, R. Krishnapuram. Mining Web access logs using relational competitive fuzzy clustering. To appear in the Proceedings of the Eight International Fuzzy Systems Association World Congress, August 1999.
    Google ScholarLocate open access versionFindings
  • M. O'Conner, J. Herlocker. Clustering items for collaborative filtering. In Proceedings of the ACM SIGIR Workshop on Recommender Systems, Berkeley, CA, 1999.
    Google ScholarLocate open access versionFindings
  • M. Perkowitz and O. Etzioni. Adaptive Web sites: automatically synthesizing Web pages. In Proceedings of Fifteenth National Conference on Artificial Intelligence, Madison, WI, 1998.
    Google ScholarLocate open access versionFindings
  • M. Spiliopoulou and L. C. Faulstich. WUM: A Web Utilization Miner. In Proceedings of EDBT Workshop WebDB98, Valencia, Spain, LNCS 1590, Springer Verlag, 1999.
    Google ScholarLocate open access versionFindings
  • M. Spiliopoulou, C. Pohle, and L. C. Faulstich. Improving the effectiveness of a Web site with Web usage mining. In Workshop on Web Usage Analysis and User Profiling (WebKKD99), San Diego, August 1999. -11-
    Google ScholarFindings
  • J. Srivastava, R. Cooley, M. Deshpande, P-T. Tan. Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data. To appear in SIGKDD Explorations, (1) 2, 2000.
    Google ScholarLocate open access versionFindings
  • S. Schechter, M. Krishnan, and M. D. Smith. Using path profiles to predict HTTP requests. In Proceedings of 7th International World Wide Web Conference, Brisbane, Australia, 1998.
    Google ScholarLocate open access versionFindings
  • U. Shardanand, P. Maes. Social information filtering: algorithms for automating "word of mouth." In Proceedings of the ACM CHI Conference, 1995.
    Google ScholarLocate open access versionFindings
  • C. Shahabi, A. Zarkesh, J. Adibi, and V. Shah. Knowledge discovery from users Web-page navigation. In Proceedings of Workshop on Research Issues in Data Engineering, Birmingham, England, 1997.
    Google ScholarLocate open access versionFindings
  • T. Yan, M. Jacobsen, H. Garcia-Molina, and U. Dayal. From user access patterns to dynamic hypertext linking. In Proceedings of the 5th International World Wide Web Conference, Paris, France, 1996.
    Google ScholarLocate open access versionFindings
  • O. R. Zaiane, M. Xin, and J. Han. Discovering web access patterns and trends by applying OLAP and data mining technology on web logs. In Advances in Digital Libraries, pp. 19-29, Santa Barbara, 1998.
    Google ScholarLocate open access versionFindings
  • P. S. Yu. Data mining and personalization technologies. In Int'l Conference on Database Systems for Advanced Applications (DASFAA99), April 1999, Hsinchu, Taiwan.
    Google ScholarLocate open access versionFindings
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