Abstract
Visitors enter a website through a variety of means, including web searches, links from other sites, and personal bookmarks. In some cases the first page loaded satisfies the visitor’s needs and no additional navigation is necessary. In other cases, however, the visitor is better served by content located elsewhere on the site found by navigating links. If the path between a user’s current location and his eventual goal is circuitous, then the user may never reach that goal or will have to exert considerable effort to reach it. By mining site access logs, we can draw conclusions of the form “users who load page p are likely to later load page q.” If there is no direct link from p to q, then it is advantageous to provide one. The process of providing links to users’ eventual goals while skipping over the in-between pages is called shortcutting. Existing algorithms for shortcutting require substantial offline training, which make them unable to adapt when access patterns change between training sessions. We present improved online algorithms for shortcut link selection that are based on a novel analogy drawn between shortcutting and caching. In the same way that cache algorithms predict which memory pages will be accessed in the future, our algorithms predict which web pages will be accessed in the future. Our algorithms are very efficient and are able to consider accesses over a long period of time, but give extra weight to recent accesses. Our experiments show significant improvement in the utility of shortcut links selected by our algorithm as compared to those selected by existing algorithms.
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References
Anderson, C.R., Domingos, P., Weld, D.S.: Adaptive web navigation for wireless devices. In: Proceedings of the 17th International Joint Conference on Artificial Intelligence (2001)
Anderson, C.R., Horvitz, E.: Web montage: A dynamic personalized start page. In: WWW 2002. Proceedings of the eleventh international conference on World Wide Web, pp. 704–712. ACM Press, New York (2002)
Banerjee, A., Ghosh, J.: Clickstream clustering using weighted longest common subsequences. In: Proc. of the Workshop on Web Mining, SIAM Conference on Data Mining, pp. 33–40 (2001)
Cherkasova, L.: Improving www proxies performance with greedy-dual-size-frequency caching policy. HP Laboratories Report No. HPL-98-69R1 (1998)
Cooley, R., Mobasher, B., Srivastava, J.: Web mining: Information and pattern discovery on the world wide web. In: ICTAI 1997. Proceedings of the 9th IEEE International Conference on Tools with Artificial Intelligence, IEEE, Los Alamitos (1997)
Eirinaki, M., Vazirgiannis, M.: Web mining for web personalization. ACM Trans. Inter. Tech. 3(1), 1–27 (2003)
Gabrilovich, E., Dumais, S., Horvitz, E.: Newsjunkie: Providing personalized newsfeeds via analysis of information novelty. In: WWW 2004. Proceedings of the 13th international conference on World Wide Web, pp. 482–490. ACM Press, New York (2004)
Megiddo, N., Modha, D.S.: Outperforming LRU with an adaptive replacement cache algorithm. Computer 37(4), 58–65 (2004)
Milic-Frayling, N., Jones, R., Rodden, K., Smyth, G., Blackwell, A., Sommerer, R.: Smartback: Supporting users in back navigation. In: WWW 2004. Proceedings of the 13th international conference on World Wide Web, pp. 63–71. ACM Press, New York (2004)
Perkowitz, M.: Adaptive Web Sites: Cluster Mining and Conceptual Clustering for Index Page Synthesis. PhD thesis, University of Washington (2001)
Perkowitz, M., Etzioni, O.: Adaptive web sites: an ai challenge. In: Proceedings of the 15th International Joint Conference on Artificial Intelligence (1997)
Perkowitz, M., Etzioni, O.: Towards adaptive web sites: Conceptual framework and case study. Artificial Intelligence 118(1-2), 245–275 (2000)
Srikant, R., Yang, Y.: Mining web logs to improve website organization. In: WWW 2001. Proceedings of the tenth international conference on World Wide Web, pp. 430–437. ACM Press, New York (2001)
Yahoo!, Inc. My Yahoo!, http://my.yahoo.com
Yang, Q., Wang, H., Zhang, W.: Web-log mining for quantitative temporal-event prediction. IEEE Computational Intelligence Bulletin 1(1), 10–18 (2002)
Yang, Q., Zhang, H.H.: Web-log mining for predictive web caching. IEEE Transactions on Knowledge and Data Engineering 15(4), 1050–1053 (2003)
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Brickell, J., Dhillon, I.S., Modha, D.S. (2007). Adaptive Website Design Using Caching Algorithms. In: Nasraoui, O., Spiliopoulou, M., Srivastava, J., Mobasher, B., Masand, B. (eds) Advances in Web Mining and Web Usage Analysis. WebKDD 2006. Lecture Notes in Computer Science(), vol 4811. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77485-3_1
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DOI: https://doi.org/10.1007/978-3-540-77485-3_1
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