article

Web mining for web personalization

Authors:

Magdalini Eirinaki,

Michalis VazirgiannisAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 3, Issue 1

Pages 1 - 27

https://doi.org/10.1145/643477.643478

Published: 01 February 2003 Publication History

Get Access

Abstract

Web personalization is the process of customizing a Web site to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user's navigational behavior (usage data) in correlation with other information collected in the Web context, namely, structure, content, and user profile data. Due to the explosive growth of the Web, the domain of Web personalization has gained great momentum both in the research and commercial areas. In this article we present a survey of the use of Web mining for Web personalization. More specifically, we introduce the modules that comprise a Web personalization system, emphasizing the Web usage mining module. A review of the most common methods that are used as well as technical issues that occur is given, along with a brief overview of the most popular tools and applications available from software vendors. Moreover, the most important research initiatives in the Web usage mining and personalization areas are presented.

References

[1]

Berendt, B. 2000. Web usage mining, site semantics, and the support of navigation. In Proceedings of the Workshop WEBKDD'2000 Web Mining for E-Commerce---Challenges and Opportunities, Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Boston, August).]]

Google Scholar

[2]

Berendt, B. 2001. Understanding Web usage at different levels of abstraction: Coarsening and visualizing sequences. In Proceedings of the Workshop WEBKDD 2001 Mining Log Data Across All Customer TouchPoints, Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Francisco, August).]]

Google Scholar

[3]

Borges, J. and Levene, M. 1999. Data mining of user navigation patterns. In Web Usage Analysis and User Profiling, Lecture Notes in Computer Science, vol. 1836, Springer-Verlag New York, 92--111.]]

Digital Library

Google Scholar

[4]

Buchner, A. and Mulvenna, M. D. 1998. Discovering Internet marketing intelligence through on line analytical Web usage mining. SIGMOD Rec. 27, 4, 54--61.]]

Digital Library

Google Scholar

[5]

Buchner, A. G., Baumgarten, M., Anand, S. S., Mulvenna, M. D., and Hughes, J. G. 1999. Navigation pattern discovery from Internet data. In Proceedings of the Web Usage Analysis and User Profiling Workshop (WEBKDD'99), Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Diego, August), 25--30.]]

Digital Library

Google Scholar

[6]

Chen, M. S., Park, J. S., and Yu, P. S. 1996. Data mining for path traversal patterns in a web environment. In Proceedings of the Sixteenth International Conference on Distributed Computing Systems (May), 385--392.]]

Digital Library

Google Scholar

[7]

Cingil, I., Dogac, A., and Azgin, A. 2000. A broader approach to personalization. Commun. ACM, 43, 8 (August), 136--141.]]

Digital Library

Google Scholar

[8]

Coenen, F., Swinnen, G., Vanhoof, K., and Wets, G. 2000. A framework for self adaptive websites: Tactical versus strategic changes. In proceedings of WEBKDD'2000 Web Mining for E-Commerce---Challenges and Opportunities, Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Boston, August).]]

Google Scholar

[9]

Cooley, R., Mobasher, B., and Srivastava, J. 1999a. Data preparation for mining world wide web browsing patterns. Knowl. Inf. Syst., 1, 1 (Feb.).]]

Digital Library

Google Scholar

[10]

Cooley, R., Tan, P.-N., and Srivastava, J. 1999b. WebSIFT: The web site information filter system. In Proceedings of the Web Usage Analysis and User Profiling Workshop (WEBKDD'99), Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Diego, August).]]

Google Scholar

[11]

Dean, R. 1998. Personalizing your web site. Available at http://Webbuilder.netscape.com/Web-building/pages/Business/Personal/index.html.]]

Google Scholar

[12]

Huang, Z., Ng, J., Cheung, D. W., Ng, M. K., and Ching, W.-K. 2001. A cube model for web access sessions and cluster analysis. In Proceedings of the Mining Log Data Across All Customer TouchPoints Workshop (WEBKDD'01), Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Francisco, August).]]

Digital Library

Google Scholar

[13]

Joachims, T., Freitag, D., and Mitchell, T. 1997. WebWatcher: A tour guide for the world wide web. In Proceedings of IJCAI97 (August).]]

Google Scholar

[14]

Joshi, A., Joshi, K., and Krishnapuram, R. 2000. On mining web access logs. In Proceedings of the 2000 ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, 63--69.]]

Google Scholar

[15]

Krishnapuram, R., Joshi, A., Nasraoui, O., and Yi, L. 2001. Low-complexity fuzzy relational clustering algorithms for web mining, IEEE Trans. Fuzzy Syst. 9, 4, 596--607.]]

Digital Library

Google Scholar

[16]

Lieberman, H. 1995. Letizia: An agent that assists web browsing. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (Montreal).]]

Google Scholar

[17]

Masseglia, F., Poncelet, P., and Cicchetti, R. 1999a. WebTool: An integrated framework for data mining. In Proceedings of the Ninth International Conference on Database and Expert Systems Applications (DEXA'99) (Florence, Italy, August), 892--901.]]

Digital Library

Google Scholar

[18]

Masseglia, F., Poncelet, P., and Teisseire, M. 1999b. Using data mining techniques on web access logs to dynamically improve hypertext structure. In ACM SigWeb Lett., 8, 3 (Oct.) 13--19.]]

Digital Library

Google Scholar

[19]

Masseglia, F., Poncelet, P., and Teisseire, M. 2000. Web usage mining: How to efficiently manage new transactions and new customers. In Proceedings of the Fourth European Conference on Principles of Data Mining and Knowledge Discovery (PKDD'00) (Lyon, France, Sept.).]]

Digital Library

Google Scholar

[20]

Mladenic, D. 1999. Machine learning used by personal webwatcher. In Proceedings Of ACAI-99 Workshop on Machine Learning and Intelligent Agents (Chania, Greece, July).]]

Google Scholar

[21]

Mobasher, B., Cooley, R., and Srivastava, J. 1999. Creating adaptive web sites through usage-based clustering of URLs. In Proceedings of the 1999 IEEE Knowledge and Data Engineering Exchange Workshop (KDEX'99) (Nov.).]]

Digital Library

Google Scholar

[22]

Mobasher, B., Cooley, R., and Srivastava, J. 2000a. Automatic personalization based on web usage mining. Commun. ACM, 43 8 (August), 142--151.]]

Digital Library

Google Scholar

[23]

Mobasher, B., Dai, H., Luo, T., Sung, Y., and Zhu, J. 2000b. Discovery of aggregate usage profiles for web personalization. In Proceedings of the Web Mining for E-Commerce Workshop (WEBKDD'2000), Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Boston, August).]]

Google Scholar

[24]

Mobasher, B., Dai, H., Luo, T., Sung, Y., and Zhu, J. 2000c. Integrating web usage and content mining for more effective personalization. In Proceedings of the International Conference on E-Commerce and Web Technologies (ECWeb2000). (Greenwich, UK, Sept.).]]

Digital Library

Google Scholar

[25]

Mulvenna, M. D., Anand, S. S., and Buchner, A. G. 2000. Personalization on the net using web mining. Commun. ACM, 43, 8 (August), 123--125.]]

Digital Library

Google Scholar

[26]

Nasraoui, O., Frigui, H., Krishnapuram, R., and Joshi, A. 2000. Extracting web user profiles using relational competitive fuzzy clustering. Int. J. Arti. Intell. Tools 9, 4.]]

Google Scholar

[27]

P3P. Platform for Privacy Preferences Project. Available at http://www.w3.org/P3P.]]

Google Scholar

[28]

Perkowitz, M. and Etzioni, O. 1997. Adaptive web sites: An AI challenge. In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (Nagoya, Japan).]]

Google Scholar

[29]

Perkowitz, M. and Etzioni, O. 1998. Adaptive web sites: Automatically synthesizing web pages. In Proceedings of the Fifteenth National Conference on Artificial Intelligence (Madison, WI, July).]]

Digital Library

Google Scholar

[30]

Perkowitz, M. and Etzioni, O. 1999. Adaptive web sites: Conceptual cluster mining. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI99) (Stockholm).]]

Digital Library

Google Scholar

[31]

Perkowitz, M. and Etzioni, O. 2000a. Towards adaptive web sites: Conceptual framework and case study. In Artif. Intell. 118, 1--2, 245--275.]]

Digital Library

Google Scholar

[32]

Perkowitz, M. and Etzioni, O. 2000b. Adaptive web sites. Commun. ACM, 43, 8 (August), 152--158.]]

Digital Library

Google Scholar

[33]

RFC. Identification Protocol. Available at http://www.rfc-editor.org/rfc/rfc1413.txt.]]

Google Scholar

[34]

Shahabi, C., Zarkesh, A. M., Adibi, J., and Shah, V. 1997. Knowledge discovery for users web-page navigation. In Workshop on Research Issues in Data Engineering (Birmingham, UK).]]

Digital Library

Google Scholar

[35]

Spiliopoulou, M. 2000. Web usage mining for web site evaluation. Commun. ACM 43, 8 (August), 127--134.]]

Digital Library

Google Scholar

[36]

Spiliopoulou, M. and Faulstich, L. C. 1998. WUM: A web utilization miner. In Proceedings of the International Workshop on the Web and Databases (Valencia, March).]]

Google Scholar

[37]

Spiliopoulou, M., Faulstich, L. C., and Wilkler, K. 1999. A data miner analyzing the navigational behavior of web users. In Proceedings of the Workshop on Machine Learning in User Modelling of the ACAI99 (Chania, Greece, July).]]

Google Scholar

[38]

Srivastava, J., Cooley, R., Deshpande, M., and Tan, P.-N. 2000. Web usage mining: Discovery and applications of usage patterns from web data. SIGKDD Explorations 1, 2 (Jan.), 12--23.]]

Digital Library

Google Scholar

[39]

W3Clog. Extended log file format. Available at http://www.w3.org/TR/WD-logfile.html.]]

Google Scholar

[40]

WCA. Web characterization terminology & definitions. Available at http://www.w3.org/ 1999/05/WCA-terms/.]]

Google Scholar

[41]

Wu, K.-L., Yu, P. S., and Ballman, A. 1998. SpeedTracer: A web usage mining and analysis tool. IBM Syst. J. 37, 1.]]

Digital Library

Google Scholar

[42]

Yan, T. W., Jacobsen, M., Garcia-Mollina, H., and Dayal, U. 1996. From user access patterns to dynamic hypertext linking. In Fifth International World Wide Web Conference (WWW5) (Paris).]]

Digital Library

Google Scholar

[43]

Zaiane, O. R., Xin, M., and Han, J. 1998. Discovering web access patterns and trends by applying OLAP and data mining technology on web logs. In Proceedings of Advances in Digital Libraries Conference (ADL'98) (Santa Barbara, CA, April).]]

Digital Library

Google Scholar

Cited By

View all

Demirağ F(2024)Web PersonalizationGlobalized Consumer Insights in the Digital Era10.4018/979-8-3693-3811-7.ch008(169-185)Online publication date: 5-Mar-2024
https://doi.org/10.4018/979-8-3693-3811-7.ch008
Topaloglu BOztaysi BDogan O(2024)Cart-State-Aware Discovery of E-Commerce Visitor Journeys with Process MiningJournal of Theoretical and Applied Electronic Commerce Research10.3390/jtaer1904013819:4(2851-2879)Online publication date: 17-Oct-2024
https://doi.org/10.3390/jtaer19040138
Li WPatras PChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Stable-Sketch: A Versatile Sketch for Accurate, Fast, Web-Scale Data Stream ProcessingProceedings of the ACM Web Conference 202410.1145/3589334.3645581(4227-4238)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645581
Show More Cited By

Index Terms

Web mining for web personalization

Recommendations

Building Web Personalization System with Time-Driven Web Usage Mining
WCI '15: Proceedings of the Third International Symposium on Women in Computing and Informatics

Web personalization is a powerful tool used for personalizing the Websites. The personalization system aims at suggesting the Web pages to the users based on their navigational patterns. Use of attributes such as time, popularity of Web objects makes ...
Web personalization based on usage mining
FDIA'09: Proceedings of the Third BCS-IRSG conference on Future Directions in Information Access

Personalized or recommender systems are a particular type of information filtering applications. User profiles, representing the information needs and preferences of users, can be inferred from log or clickthrough data, or the ratings that users provide ...
A web personalization system based on web usage mining techniques
WWW Alt. '04: Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters

In the past few years, web usage mining techniques have grown rapidly together with the explosive growth of the web, both in the research and commercial areas. In this work we present a Web mining strategy for Web personalization based on a novel ...

Reviews

Reviewer: Dimitrios Katsaros

The issue of using data mining technologies for the purpose of personalizing a Web site according to the needs or preferences of the user is the focus of this paper. It presents a survey of the most important commercially available products and research efforts for Web usage mining. The major contribution of this paper is the fact that it is the first to provide a survey of this topic. Thus, the paper may prove useful to anyone who would like to start an investigation along similar lines. The negative aspect of the work is that the critical evaluation of the solutions presented is not very satisfactory since it does not highlight the merits and disadvantages of the techniques discussed. Instead, the paper confines itself to only a short summary of their functionality. The heart of the paper should have been the categorization of the solutions presented. The references provided are adequate and appropriate, although the authors missed some references that do not deal explicitly with Web personalization, but with Web prefetching; this type of paper presents methods used to identify patterns of Web site usage. Overall, the paper contains an extensive and satisfactory list of products (commercial and research prototypes), but does not provide a concise, clear, and informative categorization of them. Online Computing Reviews Service

Access critical reviews of Computing literature here

Become a reviewer for Computing Reviews.

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 3, Issue 1

February 2003

92 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/643477

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 February 2003

Published in TOIT Volume 3, Issue 1

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

529
Total Citations
View Citations
17,580
Total Downloads

Downloads (Last 12 months)78
Downloads (Last 6 weeks)11

Reflects downloads up to 05 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Demirağ F(2024)Web PersonalizationGlobalized Consumer Insights in the Digital Era10.4018/979-8-3693-3811-7.ch008(169-185)Online publication date: 5-Mar-2024
https://doi.org/10.4018/979-8-3693-3811-7.ch008
Topaloglu BOztaysi BDogan O(2024)Cart-State-Aware Discovery of E-Commerce Visitor Journeys with Process MiningJournal of Theoretical and Applied Electronic Commerce Research10.3390/jtaer1904013819:4(2851-2879)Online publication date: 17-Oct-2024
https://doi.org/10.3390/jtaer19040138
Li WPatras PChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Stable-Sketch: A Versatile Sketch for Accurate, Fast, Web-Scale Data Stream ProcessingProceedings of the ACM Web Conference 202410.1145/3589334.3645581(4227-4238)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645581
A HD A(2024)Cluster-Based Risk Analysis For Big Data Security Framework2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT)10.1109/ICCPCT61902.2024.10673052(1085-1090)Online publication date: 8-Aug-2024
https://doi.org/10.1109/ICCPCT61902.2024.10673052
Aldalur I(2024)Web Augmentation: A systematic mapping studyScience of Computer Programming10.1016/j.scico.2023.103045232(103045)Online publication date: Jan-2024
https://doi.org/10.1016/j.scico.2023.103045
Cuzzocrea AFadda E(2024)Modeling and supporting adaptive Complex Data-Intensive Web Systems via XML and the O-O paradigm: The OO-XAHM modelArray10.1016/j.array.2024.10036323(100363)Online publication date: Sep-2024
https://doi.org/10.1016/j.array.2024.100363
Thakur NCui SPatel KHall IDuggal Y(2023)A Large-Scale Dataset of Search Interests Related to Disease X Originating from Different Geographic RegionsData10.3390/data81101638:11(163)Online publication date: 26-Oct-2023
https://doi.org/10.3390/data8110163
Han Y(2023)The Discovery of Individuality in the Process of Personalization RecommendationProceedings of the 2023 9th International Conference on Humanities and Social Science Research (ICHSSR 2023)10.2991/978-2-38476-092-3_107(881-887)Online publication date: 8-Sep-2023
https://doi.org/10.2991/978-2-38476-092-3_107
Ramadhan H(2023)Identifying Candidate Pages for Shortcuts in a Website for Site Personalization and Adaptation2023 Fourth International Conference on Intelligent Data Science Technologies and Applications (IDSTA)10.1109/IDSTA58916.2023.10317840(54-58)Online publication date: 24-Oct-2023
https://doi.org/10.1109/IDSTA58916.2023.10317840
Bashir SKhattak A(2023)Private Web Search Using Proxy-Query Based Query Obfuscation SchemeIEEE Access10.1109/ACCESS.2023.323500011(3607-3625)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3235000
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Building Web Personalization System with Time-Driven Web Usage Mining

Web personalization based on usage mining

A web personalization system based on web usage mining techniques

Reviews

Access critical reviews of Computing literature here