DETECTING PARALLEL BROWSING TO IMPROVE WEB PREDICTIVE MODELING

Geoffray Bonnin, Armelle Brun, Anne Boyer

Abstract

Present-day web browsers possess several features that facilitate browsing tasks. Among these features, one of the most useful is the possibility of using tabs. Nowadays, it is very common for web users to use several tabs and to switch from one to another while navigating. Taking into account parallel browsing is thus becoming very important in the frame of web usage mining. Although many studies about web users’ navigational behavior have been conducted, few of these studies deal with parallel browsing. This paper is dedicated to such a study. Taking into account parallel browsing involves to have some information about when tab switches are performed in user sessions. However, navigation logs usually do not contain such informations and parallel sessions appear in a mixed fashion. Therefore, we propose to get this information in an implicit way. We thus propose the TABAKO model, which is able to detect tab switches in raw navigation logs and to benefit from such a knowledge in order to improve the quality of web recommendations.

References

  1. Agrawal, R., Imielinski, T., and Swami, A. (1993). Mining Association Rules between Sets of Items in Large Databases. In Buneman, P. and Jajodia, S., editors, Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pages 207-216.
  2. Agrawal, R. and Srikant, R. (1995). Mining Sequential Patterns. In ICDE'95: Proceedings of the International Conference on Data Engineering, pages 3-14.
  3. Ayres, J., Flannick, J., Gehrke, J., and Yiu, T. (2002). Sequential Pattern Mining Using a Bitmap Representation. In KDD 7802: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 429-435.
  4. Bonnin, G., Brun, A., and Boyer, A. (2009). A LowOrder Markov Model integrating Long-Distance Histories for Collaborative Recommender Systems. In Proceedings of the 13th International Conference on Intelligent user interfaces (IUI), pages 57-66.
  5. Deshpande, M. and Karypis, G. (2004). Selective Markov Models for Predicting Web Page Accesses. ACM Trans. Internet Technol., 4(2):163-184.
  6. Gündüz, S. and O zsu, M. (2003). A Web Page Prediction Model Based on Click-Stream Tree Representation of User Behavior. In KDD 7803: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 535-540.
  7. Jianyong, W., Jiawei, H., and Li, C. (2007). Frequent Closed Sequence Mining without Candidate Maintenance. IEEE Transactions on Knowledge and Data Engineering, 19(8):1042-1056.
  8. Jin, X., Zhou, Y., and Mobasher, B. (2005). Task-oriented Web User Modeling for Recommendation. In Proceedings of the 10th International Conference on User Modeling (UM), pages 109-118.
  9. Lu, L., Dunham, M., and Meng, Y. (2005). Mining Significant Usage Patterns from Clickstream Data. In 7th International Workshop on Knowledge Discovery on the Web, pages 1-17.
  10. Nakagawa, M. and Mobasher, B. (2003). Impact of Site Characteristics on Recommendation Models Based On Association Rules and Sequental Patterns. In Intelligent Techniques for Web Personalization.
  11. Needleman, S. and Wunsch, C. (1970). A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of molecular biology, 48(3):443-453.
  12. Pitkow, J. and Pirolli, P. (1999). Mining Longest Repeating Subsequences to Predict World Wide Web Surfing. In USITS'99: Proceedings of the 2nd conference on USENIX Symposium on Internet Technologies and Systems, pages 139-150.
  13. Schechter, S., Krishnan, M., and Smith, M. (1998). Using Path Profiles to Predict HTTP Requests. Computer Networks and ISDN Systems, 30(1-7):457-467.
  14. Smith, T. and Waterman, M. (1981). Identification Of Common Molecular Subsequences.
  15. Srivastava, J., Cooley, R., Deshpande, M., and Tan, P. (2000). Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data. SIGKDD Explorations Newsletter, 1(2):12-23.
  16. Tan, P. and Kumar, V. (2002). Discovery of Web Robot Sessions Based on their Navigational Patterns. Data Mining Knowledge Discovery, 6(1):9-35.
  17. Viermetz, M., Stolz, C., Gedov, V., and Skubacz, M. (2006). Relevance and Impact of Tabbed Browsing Behavior on Web Usage Mining. In Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pages 262-269.
  18. Weinreich, H., Obendorf, H., Herder, E., and Mayer, M. (2006). Off the Beaten Tracks: Exploring Three Aspects of Web Navigation. In WWW 7806: Proceedings of the 15th international conference on World Wide Web, pages 133-142.
Download


Paper Citation


in Harvard Style

Bonnin G., Brun A. and Boyer A. (2010). DETECTING PARALLEL BROWSING TO IMPROVE WEB PREDICTIVE MODELING . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010) ISBN 978-989-8425-28-7, pages 504-509. DOI: 10.5220/0003115905040509


in Bibtex Style

@conference{kdir10,
author={Geoffray Bonnin and Armelle Brun and Anne Boyer},
title={DETECTING PARALLEL BROWSING TO IMPROVE WEB PREDICTIVE MODELING},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010)},
year={2010},
pages={504-509},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003115905040509},
isbn={978-989-8425-28-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010)
TI - DETECTING PARALLEL BROWSING TO IMPROVE WEB PREDICTIVE MODELING
SN - 978-989-8425-28-7
AU - Bonnin G.
AU - Brun A.
AU - Boyer A.
PY - 2010
SP - 504
EP - 509
DO - 10.5220/0003115905040509