Abstract
Nowadays, most websites provide tremendous information organized in complex structures of web pages. Therefore, how to help users quickly find pages they are looking for is an important issue. Although a sitemap can provide navigation information across sections of the website, it is static and can hardly provide dynamic information based on access patterns and browsing trends. In this paper, we proposed a hybrid approach for improving recommendation ranking of the web pages for the next visit. Our raking strategy considers not only the relevance (correlation to the next page calculated by the collaborative filtering algorithm) but also the level of interest (time spent on a page) and accessibility (the distance to the next page). In order to evaluate the proposed recommendation ranking algorithm, we used the web access log (IIS log) of a website, Health 99, operated by the Bureau of Health Promotion, Taiwan. The log data was divided into training and testing sets. The measurements of the relevance, the level of interest and the distance factor were computed from the training set. The experimental results showed that the possibility of the pages in the recommendation ranking lists by our approach that were accepted by users was much higher than that proposed by the original collaborative filtering algorithm, particular in short recommendation list (< 5).
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References
Cline, R.J., Haynes, K.M.: Consumer health information seeking on the Internet: the state of the art. Health Educ. Res. 16(6), 671–692 (2001)
McKinley, J., Cattermole, H., Oliver, C.W.: The quality of surgical information on the Internet. Journal of the Royal College of Surgeons of Edinsburgh 44, 256–268 (1999)
Jacobson, R.L.: Taming the Internet. The Chronicle of Higher Education 41(32), A29–A30 (1995)
Pereira, J., Bruera, R.: The Internet as a resource for palliative care and hospice: a review and propasals. Journal of Pain and Symptom Management 16(1), 59–68 (1998)
Srikant, R., Yang, Y.: Mining web logs to improve website organization. In: World Wide Web, pp. 430–437 (2001)
Garofalakis, J., Kappos, P., Mourloukos, D.: Web site optimization using page popularity. IEEE Internet Computing, 22–29 (July-August 1999)
Perkowitz, M., Etzioni, O.: Adaptive Web sites: automatically learning from user access patterns. In: Proc. 6th Int. World Wide Web Conference, Santa Clara, CA (1997)
Nichols, D.M.: Implicit Rating and Filtering. In: Proceedings of the 5th DELOS Workshop on Filtering and Collaborative Filtering, pp. 31–36 (1997)
Wu, C.-H.: A New Approach of Auto-recommendation using Implict Feedback (unpublished)
Morita, M., Shinoda, Y.: Information Filtering Based on User Behavior Analysis and Best Match Text Retrieval. In: Proceeding of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 272–281 (1994)
Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordan, L.R., Riedl, J.: GroupLens: Applying Collaborative Filtering to Usenet News. Communication of ACM 40(3), 77–87 (1997)
Oard, D.W., Kim, J.: Implicit Feedback for Recommender System. In: Proceeding of the AAAI Workshop on Recommender System, pp. 81–83 (1998)
Oard, D.W., Kim, J.: Modeling information content using observable behavior. In: Proceeding of the 64th Annual Conference of the America Society for Information Science and Technology Annual Meeting, pp. 481–488 (2001)
Sarwar, B., Karypis, G., Konstan, J., Reidl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, Hong Kong, May 01-05, pp. 285–295 (2001)
Liu, B.: Web Data Mining, 1st edn., pp. 480–482. UOS Press, Springfield (2007)
Shan, M.L.: Experimental Design, 1st edn., pp. 421–423. Jeou Chou Book Co, Ltd. (1993)
Su, C.-P.: Apply Web Mining Techniques to Analyse the Navigation Behaviour of Visitors-Using Online Content Site as Example (unpublished)
Cooley, R., Mobasher, B., Srivastava, J.: Data preparation for mining World Wide Web browsing patterns. Knowledge and Information Systems 1(1) (1999)
Yu, J.X., Ou, Y., Zhang, C., Zhang, S.: Identifying interesting visitors through web log classification. IEEE Intelligent Systems 20, 55–59 (2005)
Department of Health Executive Yuan Bureau of Health Promotion Health 99 Education Resouce website, http://health99.doh.gov.tw/en/index.aspx
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of Recommendation Algorithm for E-Commerce. In: Proc. ACM Conference on Electronic Commerce, Minnesota, pp. 158–167 (October 2000)
Shen, L., Zhou, Y.: A Ner User Similarity Measure for Collaborative Filtering Algorithm. In: International Conference on Computer Modeling and Simulation, pp. 375–378 (2010)
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Hu, SC., Yang, CY., Liu, CT. (2012). A Hybrid Item-Based Recommendation Ranking Algorithm Based on User Access Patterns. In: Zhang, W. (eds) Advanced Technology in Teaching. Advances in Intelligent and Soft Computing, vol 163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29458-7_35
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DOI: https://doi.org/10.1007/978-3-642-29458-7_35
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