Download Adaptive and Personalized Semantic Web by Christos Makris, Yannis Panagis (auth.), Spiros Sirmakessis PDF

By Christos Makris, Yannis Panagis (auth.), Spiros Sirmakessis Dr. (eds.)

Web Personalization will be outlined as any set of activities which could tailor the net adventure to a specific consumer or set of clients. to accomplish powerful personalization, organisations needs to depend on all on hand facts, together with the utilization and click-stream information (reflecting person behaviour), the location content material, the location constitution, area wisdom, in addition to person demographics and profiles. furthermore, effective and clever thoughts are had to mine this information for actionable wisdom, and to successfully use the came across wisdom to augment the clients' net event. the purpose of the overseas Workshop on Adaptive and customized Semantic net that was once held within the 16th ACM convention on Hypertext and Hypermedia (September 6-9, 2005, Salzburg, Austria) was once to assemble researchers and practitioners within the fields of net engineering, adaptive hypermedia, semantic net applied sciences, wisdom administration, details retrieval, person modelling, and different comparable disciplines which offer allowing applied sciences for personalisation and model at the world-wide-web. The e-book includes the papers offered in the course of the workshop. shows of the papers can be found on-line at www.hci.gr.

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Wum: A web utilization miner. In Proceedings of EDBT workshop WebDB98, Valencia, Spain, 1999. 43. J. Srivastava, R. Cooley, M. N. Tan. Web usage mining: Discovery and applications of usage patterns from web data. SIGKDD Explorations, 1(2):1–12, Jan 2000. A Multi-Layered and Multi-Faceted Framework 35 44. L. Terveen, W. Hill, and B. Amento. Phoaks – a system for sharing recommendations. Comm. ACM, 40(3), 1997. 45. T. Yan, M. Jacobsen, H. Garcia-Molina, and U. Dayal. From user access patterns to dynamic hypertext linking.

On Very Large Data Bases (VLDB’03), Berlin, Germany, Sept 2003. 4. S. Babu and J. Widom. Continuous queries over data streams. In SIGMOD Record’01, pp. 109–120, 2001. 5. M. Balabanovic and Y. Shoham. Fab: Content-based, collaborative recommendation. Communications of the ACM, 40(3):67–72, 1997. 6. D. Barbara. Requirements for clustering data streams. ACM SIGKDD Explorations Newsletter, 3(2):23–27, 2002. 7. J. Borges and M. Levene. Data mining of user navigation patterns. A. A. S. Newton, editors, Web Usage Analysis and User Profiling, Lecture Notes in Computer Science, pp.

In this case we can resort to the tags that give an annotation of the special file, and even if the tag is not available, we can simply ignore the content of this file, because we plan to also use collaborative filtering (user-to-user recommendations) known to capture the meaning of special content files through their association with other regular files. Step 2: Integrating Content and REFERRER Information into Web Usage Mining After transforming each user sequence of clicked URLs into a sequence of title terms, we can proceed to perform web usage mining in the URL (click) domain or title term domain, and extract the profiles as a set of URLs/title terms relevant to user groups.

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