Abstract
In this chapter we present an overview of Web personalization process viewed as an application of data mining requiring support for all the phases of a typical data mining cycle. These phases include data collection and pre-processing, pattern discovery and evaluation, and finally applying the discovered knowledge in real-time to mediate between the user and the Web. This view of the personalization process provides added flexibility in leveraging multiple data sources and in effectively using the discovered models in an automatic personalization system. The chapter provides a detailed discussion of a host of activities and techniques used at different stages of this cycle, including the preprocessing and integration of data from multiple sources, as well as pattern discovery techniques that are typically applied to this data. We consider a number of classes of data mining algorithms used particularly for Web personalization, including techniques based on clustering, association rule discovery, sequential pattern mining, Markov models, and probabilistic mixture and hidden (latent) variable models. Finally, we discuss hybrid data mining frameworks that leverage data from a variety of channels to provide more effective personalization solutions.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
- Association Rule
- Recommender System
- Latent Dirichlet Allocation
- Frequent Itemsets
- Association Rule Mining
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Agarwal, R., Aggarwal, C., Prasad, V.: A Tree Projection Algorithm for Generation of Frequent Itemsets. Journal of Parallel and Distributed Computing 61(3), 350–371 (2001)
Aggarwal, C.C., Wolf, J.L., Yu, P.S.: A New Method for Similarity Indexing for Market Data. In: Proceedings of the 1999 ACM SIGMOD Conference, Philadelphia, PA, June 1999, pp. 407–418. ACM Press, New York (1999)
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proceedings of the 20th International Conference on Very Large Data Bases (VLDB’94), Santiago, Chile, September 1994, pp. 487–499 (1994)
Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of the International Conference on Data Engineering (ICDE’95), Taipei, Taiwan, pp. 3–14 (March 1995)
Anderson, C., Domingos, P., Weld, D.: Adaptive Web Navigation for Wireless Devices. In: Proceedings of the 17th International Joint Conference on Artificial Intelligence, Seattle, Washington, August 2001, pp. 879–884 (2001)
Balabanovic, M., Shohan, Y.: Fab: Content-Based, Collaborative Recommendation. Communications of the ACM 40(3), 66–72 (1997)
Banerjee, A., Ghosh, J.: Clickstream Clustering Using Weighted Longest Common Subsequences. In: Proceedings of the Web Mining Workshop at the 1st SIAM Conference on Data Mining, Chicago, Illinois (April 2001)
Bartholomem, D., Knott, M.: Latent Variable Models and Factor Analysis. Oxford University Press, New York (1999)
Basu, C., Hirsh, H., Cohen, W.: Recommendation as Classification: Using Social and Content-based Information in Recommendation. In: Mittal, V.O., Yanco, H.A., Aronis, J., Simpson, R.C. (eds.) Assistive Technology and Artificial Intelligence. LNCS (LNAI), vol. 1458, pp. 11–15. Springer, Heidelberg (1998)
Baumgarten, M., Büchner, A.G., Anand, S.S., Mulvenna, M.D., Hughes, J.: User-driven navigation pattern discovery from internet data. In: Masand, B., Spiliopoulou, M. (eds.) Web Usage Analysis and User Profiling. LNCS (LNAI), vol. 1836, pp. 74–91. Springer, Heidelberg (2000)
Belkin, N., Croft, B.: Information Filtering and Information Retrieval. Communications of ACM 35(12), 29–37 (2001)
Berendt, B., Mobasher, B., Nakagawa, M., Spiliopoulou, M.: The impact of site structure and user environment on session reconstruction in web usage analysis. In: Zaïane, O.R., Srivastava, J., Spiliopoulou, M., Masand, B. (eds.) WEBKDD 2002 - Mining Web Data for Discovering Usage Patterns and Profiles. LNCS (LNAI), vol. 2703, pp. 159–179. Springer, Heidelberg (2003)
Berendt, B., Spiliopoulou, M.: Analysis of Navigation Behaviour in Web Sites Integrating Multiple Information Systems. VLDB Journal, Special Issue on Databases and the Web 9(1), 56–75 (2000)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)
Billsus, D., Pazzani, M.J.: Learning Collaborative Information Filters. In: Proceedings of the 15th International Conference on Machine Learning (ICML’98), Madison, Wisconsin, pp. 46–53 (July 1998)
Blei, D., Ng, A., Jordan, M.: Latent dirichlet allocation. Journal of Machine Learning Research 3, 993–1022 (2003)
Borges, J., Levene, M.: Data mining of user navigation patterns. In: Masand, B., Spiliopoulou, M. (eds.) Web Usage Analysis and User Profiling. LNCS (LNAI), vol. 1836, pp. 92–111. Springer, Heidelberg (2000)
Brants, T., Chen, F., Tsochantaridis, I.: Topic-Based Document Segmentation with Probabilistic Latent Semantic Analysis. In: Proceedings of the Eleventh International Conference on Information and Knowledge Management, Washington D.C., pp. 211–218 (Nov. 2002)
Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Annual Conference on Uncertainty in Artificial Intelligence, Madison, Wisconsin, pp. 43–52 (July 1998)
Büchner, A., Mulvenna, M.D.: Discovering Internet Marketing Intelligence through Online Analytical Web Usage Mining. SIGMOD Record 4(27), 54–61 (1998)
Burke, R.: Hybrid systems for personalized recommendations. In: Mobasher, B., Anand, S.S. (eds.) ITWP 2003. LNCS (LNAI), vol. 3169, pp. 133–152. Springer, Heidelberg (2005)
Burke, R.: Hybrid web recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web: Methods and Strategies of Web Personalization. LNCS, vol. 4321, pp. 377–408. Springer, Heidelberg (2007)
Cadez, I.V., Heckerman, D., Meek, C., Smyth, P., White, S.: Model-based Clustering and Visualization of Navigation Patterns on a Web Site. Journal of Data Mining and Knowledge Discovery 7(4), 399–424 (2003)
Cadez, I., Smyth, P., Ip, E., Mannila, H.: Predictive profiles for transaction data using finite mixture models. Technical Report Technical Report No. 01–67, Information and Computer Science Department, University of California, Irvine, CA (2001)
Canny, J.: Collaborative Filtering with Privacy via Factor Analysis. In: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Tampere, Finland, pp. 238–245. ACM Press, New York (2002)
Cassel, L., Wolz, U.: Client Side Personalization. In: Proceedings of the Second DELOS Network of Excellence Workshop on Personalization and Recommender Systems in Digital Libraries, Dublin, Ireland (June 2001)
Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., Wirth, R.: Crisp-dm 1.0: Step-by-step data mining guide (2000), http://www.crisp-dm.org
Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sartin, M.: Combining content-based and collaborative filters in an online newspaper. In: Proceedings of the ACM SIGIR ’99 Workshop on Recommender Systems: Algorithms and Evaluation, Berkeley, California, ACM, New York (1999)
Cohn, D., Chang, H.: Probabilistically Identifying Authoritative Documents. In: Proceedings of the Seventeenth International Conference on Machine Learning, Stanford, CA, pp. 167–174 (June 2000)
Cohn, D., Hofmann, T.: The missing link: A probabilistic model of document content and hypertext connectivity. In: Todd, K., Leen, T.G.D., Tresp, V. (eds.) Advances in Neural Information Processing Systems 13, pp. 430–436. MIT Press, Vancouver (2001)
Cooley, R., Mobasher, B., Srivastava, J.: Web Mining: Information and Pattern Discovery on the World Wide Web. In: Proceedings of the 9th IEEE International Conference on Tools with Artificial Intelligence (ICTAI’97), Newport Beach, CA, pp. 558–567. IEEE Computer Society Press, Los Alamitos (1997)
Cooley, R., Mobasher, B., Srivastava, J.: Data Preparation for Mining World Wide Web Browsing Patterns. Journal of Knowledge and Information Systems 1(1), 5–32 (1999)
Dai, H., Mobasher, B.: A road map to more effective Web personalization: Integrating domain knowledge with Web usage mining. In: Proceedings of the International Conference on Internet Computing, IC03, Las Vegas, pp. 58–64 (June 2003)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of Royal Statistical Society B(39), 1–38 (1977)
Deshpande, M., Karypis, G.: Item-Based Top-N Recommendation Algorithms. ACM Transactions on Information Systems 22(1), 1–34 (2004)
Deshpande, M., Karypis, G.: Selective Markov Models for Predicting Web-Page Accesses. ACM Transactions on Internet Technology 4(2), 163–184 (2004)
Eirinaki, M., Vazirgiannis, M., Varlamis, I.: SEWeP: Using Site Semantics and a Taxonomy to Enhance the Web Personalization Process. In: Proceedings of the 9th SIGKDD International Conference on Data Mining and Knowledge Discovery (KDD’03), Washington, DC, pp. 99–108 (Aug. 2003)
Eveitt, B.: An Introduction to Latent Variable Models. Champman and Hall, New York (1984)
Fu, X., Budzik, J., Hammond, K.J.: Mining Navigation History for Recommendation. In: Proceedings of the 2000 International Conference on Intelligent User Interfaces, New Orleans, LA, pp. 106–112. ACM Press, New York (Jan. 2000)
Gauch, S., Speretta, M., Chandramouli, A., Micarelli, A.: User profiles for personalized information access. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web: Methods and Strategies of Web Personalization. LNCS, vol. 4321, pp. 54–89. Springer, Heidelberg (2007)
Ghani, R., Fano, A.: Building Recommender Systems Using a Knowledge Base of Product Semantics. In: Proceedings of the Workshop on Recommendation and Personalization in E-Commerce, at the 2nd Int’l Conf. on Adaptive Hypermedia and Adaptive Web Based Systems, Malaga, Spain (May 2002)
Girolami, M., Kaban, A.: On an Equivalence between PLSI and LDA. In: Proceedings of the 26th Annual International ACM SIGIR Conference (SIGIR’03), Toronto, Canada, pp. 433–434. ACM Press, New York (July 2003)
Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: A Constant Time Collaborative Filtering Algorithm. Information Retrieval 4(2), 133–151 (2001)
Griffiths, T.L., Steyvers, M.: Finding Scientific Topics. Proceedings of the National Academy of Sciences, PNAS 2004 101, 5228–5235 (April 2004)
Haase, P., Ehrig, M., Hotho, A., Schnizler, B.: Personalized Information Access in a Bibliographic Peer-to-Peer System. In: Proceedings of the AAAI Workshop on Semantic Web Personalization, AAAI Workshop Technical Report, pp. 1–12 (2004)
Han, E., Karypis, G., Kumar, V., Mobasher, B.: Hypergraph Based Clustering in High-Dimensional Data Sets: A Summary of Results. IEEE Data Engineering Bulletin 21(1), 15–22 (March 1998)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2001)
Hanley, J.A., McNeil, B.J.: The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143, 29–36 (1982)
Herlocker, J., Konstan, J., Borchers, A., Riedl, J.: An Algorithmic Framework for Performing Collaborative Filtering. In: Proceedings of the 22nd ACM Conference on Research and Development in Information Retrieval (SIGIR’99), Berkeley, CA, pp. 230–237. ACM Press, New York (Aug. 1999)
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.: Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems 22(1), 5–53 (2004)
Hofmann, T.: Probabilistic Latent Semantic Indexing. In: Proceedings of the 22nd International Conference on Research and Development in Information Retrieval, Berkeley, CA, pp. 50–57 (Aug. 1999)
Hofmann, T.: Unsupervised Learning by Probabilistic Latent Semantic Analysis. Machine Learning Journal 42(1), 177–196 (2001)
Hofmann, T.: Latent Semantic Models for Collaborative Filtering. ACM Transactions on Information Systems 22(1), 89–115 (2004)
Jin, X., Zhou, Y., Mobasher, B.: A Unified Approach to Personalization Based on Probabilistic Latent Semantic Models of Web Usage and Content. In: Proceedings of the AAAI 2004 Workshop on Semantic Web Personalization (SWP’04), San Jose, CA (2004)
Jin, X., Zhou, Y., Mobasher, B.: Web Usage Mining Based on Probabilistic Latent Semantic Analysis. In: Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD04), Seattle, WA, pp. 197–205. ACM Press, New York (Aug. 2004)
Jin, X., Zhou, Y., Mobasher, B.: Task-oriented Web User Modeling for Recommendation. In: Proceedings of the 10th International Conference on User Modeling (UM’05), Edinburgh, UK, pp. 109–118 (July 2005)
Joshi, A., Krishnapuram, R.: On Mining Web Access Logs. In: Proceedings of the ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD 2000), Dallas, Texas, ACM Press, New York (May 2000)
Karypis, G.: Evaluation of Item-Based Top-N Recommendation Algorithms. In: Proceedings of the tenth International conference on Information and knowledge management (CIKM’01), Atlanta, Georgia, pp. 247–254 (Oct. 2001)
Kearney, P., Anand, S.S., Shapcott, M.: Employing a Domain Ontology to Gain Insights into User Behaviour. In: Proceedings of the 3rd Workshop on Intelligent Techniques for Web Personalization, at IJCAI 2005, Edinburgh, Scotland (Aug. 2005)
Kim, Y., Chang, J., Zhang, B.: a Empirical Study on Dimensionality Optimization in Text Mining for Linguistic Knowledge Acquisition. In: Proceedings of the Seventh Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-03), Seol, Korea, pp. 111–116 (April 2003)
Kohavi, R., Mason, L., Parekh, R., Zheng, Z.: Lessons and Challenges from Mining Retail E-Commerce Data. Machine Learning 57(1–2), 83–113 (2004)
Kohavi, R., Provost, F.: Applications of Data Mining to Electronic Commerce. Data Mining and Knowledge Discovery 5(1–2), 5–10 (2001)
Kohrs, A., Mérialdo, B.: Clustering for Collaborative Filtering Applications. In: Proceedings of the International Conference on Computational Intelligence for Modelling, Control & Automation (CIMCA’99), Vienna, Austria (Feb. 1999)
Konstan, J., Miller, B., Maltz, D., Herlocker, J., Gordon, L., Riedl, J.: Grouplens: Applying Collaborative Filtering to Usenet News. Communications of the ACM 40(3), 77–87 (1997)
Krulwich, B.: Lifestyle Finder: Intelligent User Profiling Using Large-Scale Demographic Data. AI Magazine 18(2), 37–45 (1997)
Krulwich, B., Burkey, C.: Learning User Information interests through extraction of semantically significant phrases. In: Proceedings of the AAAI Spring Symposium on Machine Learning in Information Access, Stanford, California (March 1996)
Lam, S.K., Riedl, J.: Shilling recommender systems for fun and profit. In: Proceedings of the 13th international World Wide Web conference (WWW’04), New York, NY, pp. 393–402 (May 2004)
Lang, K.: NewsWeeder: Learning to filter netnews. In: Proceedings of the 12th International Conference on Machine Learning, Tahoe City, California, pp. 331–339 (July 1995)
Li, J., Zaiane, O.: Using Distinctive Information Channels for Mission-Based Recommender Systems. In: Proceedings of the sixth WEBKDD workshop: Webmining and Web Usage Analysis (WEBKDD04), in conjunction with the 10th ACM SIGKDD conference (KDD’04), Seattle, Washington, ACM Press, New York (Aug. 2004)
Lieberman, H.: Letizia: An Agent that Assists Web Browsing. In: Proceedings of the 14th International Joint Conference in Artificial Intelligence (IJCAI’95), Montreal, Quebec, Canada, pp. 924–929 (Aug. 1995)
Lin, W., Alvarez, S.A., Ruiz, C.: Efficient Adaptive-Support Association Rule Mining for Recommender Systems. Data Mining and Knowledge Discovery 6, 83–105 (2002)
Linden, G., Smith, B., York, J.: Amazon.com Recommendations: Item-to-Item Collaborative Filtering. IEEE Internet Computing 7(1), 76–80 (2003)
Marlin, B.: Modeling User Rating Profiles for Collaborative Filtering. In: Proceedings of the 17th Annual Conference on Neural Information Processing System (NIPS’03), Vancouver, B.C., Canada (Dec. 2003)
Massa, P., Avesani, P.: Trust-aware collaborative filtering for recommender systems. In: Proceedings of International Conference on Cooperative Information Systems, Larnaca, Cyprus, pp. 492–508 (Oct. 2004)
Melville, P., Mooney, R.J., Nagarajan, R.: Content-Boosted Collaborative Filtering. In: Proceedings of the SIGIR2001 Workshop on Recommender Systems, New Orleans, LA (Sept. 2001)
Micarelli, A., Sciarrone, F., Marinilli, M.: Web document modeling. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web: Methods and Strategies of Web Personalization. LNCS, vol. 4321, pp. 155–194. Springer, Heidelberg (2007)
Middleton, S.E., Shadbolt, N.R., Roure, D.C.D.: Ontological User Profiling in Recommender Systems. ACM Transactions on Information Systems 22(1), 54–88 (2004)
Minka, T., Lafferty, J.: Expectation-Propagation for the Generative Aspect Model. In: Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence, Edmonton, Alberta, Canada, pp. 352–359 (Aug. 2002)
Mladenic, D.: Personal web watcher: Implementation and design. Technical Report IJS-DP-7472, Department of Intelligent Systems, J. Stefan Institute, Slovenia (1996)
Mladenic, D.: Text-Learning and Related Intelligent Agents: A Survey. IEEE Intelligent Systems 14(4), 44–54 (1999)
Mobasher, B.: Web usage mining. In: Wong, J. (ed.) Encyclopedia of Data Warehousing and Data Mining, pp. 1216–1220. Idea Group Publishing, Hershey (2005)
Mobasher, B.: Web usage mining and personalization. In: Singh, M.P. (ed.) Practical Handbook of Internet Computing. CRC Press (2005)
Mobasher, B., Dai, H., Luo, T., Nakagawa, M.: Effective Personalization Based on Association Rule Discovery from Web Usage Data. In: Proceedings of the 3rd ACM Workshop on Web Information and Data Management (WIDM01), Atlanta, Georgia, ACM Press, New York (Nov. 2001)
Mobasher, B., Dai, H., Luo, T., Nakagawa, M.: Improving the Effectiveness of Collaborative Filtering on Anonymous Web Usage Data. In: Proceedings of the IJCAI 2001 Workshop on Intelligent Techniques for Web Personalization (ITWP01), Seattle, WA (Aug. 2001)
Mobasher, B., Dai, H., Luo, T., Nakagawa, M.: Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization. Data Mining and Knowledge Discovery 6(1), 61–82 (2002)
Mobasher, B., Dai, H., Luo, T., Nakagawa, M.: Using Sequential and Non-Sequential Patterns for Predictive Web Usage Mining Tasks. In: Proceedings of the IEEE International Conference on Data Mining, Maebashi City, Japan, pp. 669–672. IEEE Computer Society Press, Los Alamitos (Dec. 2002)
Mobasher, B., Dai, H., Luo, T., Sun, Y., Zhu, J.: Integrating web usage and content mining for more effective personalization. In: Bauknecht, K., Madria, S.K., Pernul, G. (eds.) EC-Web 2000. LNCS, vol. 1875, pp. 165–176. Springer, Heidelberg (2000)
Mobasher, B., Dai, H.T., Luo, M.N.: Discovery and evaluation of aggregate usage profiles for web personalization. Data Mining and Knowledge Discovery 6, 61–82 (2002)
Mulvenna, M.D., Anand, S.S., Büchner, A.G.: Personalization on the Net using Web Mining. Communication of ACM 43(8), 122–125 (2000)
Nakagawa, M., Mobasher, B.: A Hybrid Web Personalization Model Based on Site Connectivity. In: Proceedings of the WebKDD 2003 Workshop, at the ACM-SIGKDD Conference on Knowledge Discovery in Databases (KDD’2003), Washington, DC, ACM Press, New York (Aug. 2003)
Nasraoui, O., Frigui, H., Krishnapuram, R., Joshi, A.: Extracting Web User Profiles Using Relational Competitive Fuzzy Clustering. International Journal on Artificial Intelligence Tools 9(4), 509–526 (2000)
Nasraoui, O., Krishnapuram, R., Joshi, A., Kamdar, T.: Automatic web user profiling and personalization using robust fuzzy relational clustering. In: Segovia, J., Szczepaniak, P., Niedzwiedzinski, M. (eds.) Studies in Fuzziness and Soft Computing, vol. 105, pp. 233–261. Springer, Heidelberg (2002)
Niu, L., Yan, X., Zhang, a.C., Zhang, a.S.: Product hierarchy-based customer profiles for electronic commerce recommendation. In: Proceedings of the 1st International Conference on Machine Learning and Cybernetics, pp. 1075–1080 (2002)
Oberle, D., Berendt, B., Hotho, A., Gonzalez, J.: Conceptual User Tracking. In: Proceedings of the Atlantic Web Intelligence Conference (AWIC’03), Madrid, Spain, pp. 155–164 (May 2003)
O’Connor, M., Herlocker, J.: Clustering Items for Collaborative Filtering. In: Proceedings of ACM SIGIR’99 Workshop on Recommender Systems: Algorithms and Evaluation, Berkeley, California, ACM Press, New York (Aug. 1999)
O’Mahony, M., Hurley, N., Kushmerick, N., Silverstre, G.: Collaborative Recommendations: A Robustness Analysis. ACM Transactions on Internet Technologies 4(4), 344–377 (2004)
Padmanabhan, B., Tuzhilin, A.: Unexpectedness as a measure of interestingness in knowledge discovery. Decision Support Systems 27(3), 303–318 (1999)
Palpanas, T., Mendelzon, A.: Web Prefetching Using Partial Match Prediction. In: Proceedings of the 4th International Web Caching Workshop (WCW99), San Diego, CA (March 1999)
Parent, S., Mobasher, B., Lytinen, S.: An adaptive agent for web exploration based on concept hierarchies. In: Proceedings of the 9th International Conference on Human Computer Interaction, New Orleans, pp. 903–907 (Aug. 2001)
Pavlov, D.: Sequence Modeling with Mixtures of Conditional Maximum Entropy Distributions. In: Proceedings of the Third IEEE International Conference on Data Mining (ICDM’03), Melbourne, Florida, pp. 251–258. IEEE Computer Society Press, Los Alamitos (Nov. 2003)
Pazzani, M.: A Framework for Collaborative, Content-Based and Demographic Filtering. Artificial Intelligence Review 13(5-6), 393–408 (1999)
Pazzani, M., Billsus, D.: Learning and Revising User Profiles: The identification of interesting web sites. Machine Learning 27, 313–331 (1997)
Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web: Methods and Strategies of Web Personalization. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007)
Pei, J., Han, J., Mortazavi-Asl, B., Zhu, H.: Mining Access Patterns Efficiently from Web Logs. In: Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD’00), Kyoto, Japan, pp. 396–407 (April 2000)
Perkowitz, M., Etzioni, O.: Adaptive Web Sites: Automatically Synthesizing Web Pages. In: Proceedings of the 15th National Conference on Artificial Intelligence, Madison, WI, pp. 727–732 (July 1998)
Perkowitz, M., Etzioni, O.: Adaptive Web Sites. Communications of ACM 43(8), 152–158 (2000)
Pitkow, J., Pirolli, P.: Mining Longest Repeating Subsequences to Predict WWW Surfing. In: Proceedings of the 2nd USENIX Symposium on Internet Technologies and Systems, Boulder, Colorado (Oct. 1999)
Popescul, A., Ungar, L., Pennock, D., Lawrence, S.: Probabilistic Models for Unified Collaborative and Content-based Recommendation in Sparse-data Environments. In: Proceedings of 17th Conference in Uncertainty in Artificial Intelligence, Seattle, WA, pp. 437–444 (Aug. 2001)
Rissanen, J.: Modelling by Shortest Data Description. Automatica 14, 465–471 (1978)
Rivasseau, J.: Understanding and applying lda model to first-order markov chains. Univ. of british columbia, canada, technical report, Univ. of British Columbia, Canada (2003)
Rosenfeld, R.: Adaptive statistical language modeling: A maximum entropy approach. Phd dissertation, CMU (1994)
Salton, G., McGill, M.: Introduction to Modern Information Retrieval. McGraw-Hill, New York (1983)
Sarukkai, R.R.: Link Prediction and Path Analysis Using Markov Chains. In: Proceedings of the 9th International World Wide Web Conference, Amsterdam (May 2000), http://www9.org/w9cdrom/index.html
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based Collaborative Filtering Recommendation Algorithms. In: Proceedings of the 10th International WWW Conference, Hong Kong, pp. 285–295 (May 2001)
Sarwar, B.M., Karypis, G., Konstan, J., Riedl, J.: Analysis of Recommender Algorithms for E-Commerce. In: Proceedings of the 2nd ACM E-Commerce Conference (EC’00), Minneapolis, MN, pp. 158–167. ACM Press, New York (Oct. 2000)
Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Application of Dimensionality Reduction in Recommender System - A Case Study. In: Proceedings of the WebKDD 2000 Web Mining for E-Commerce Workshop at ACM SIGKDD 2000, Boston. ACM Press, New York (Aug. 2000)
Schafer, J.B., Frankowski, D., Herlocker, J.L., Sen, S.: Collaborative filtering recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web: Methods and Strategies of Web Personalization. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007)
Schafer, J.B., Konstan, J.A., Riedl, J.: Recommender Systems in E-Commerce. In: Proceedings of the ACM Conference on Electronic Commerce, Denver, Colorado, pp. 158–166. ACM Press, New York (Nov. 1999)
Schechter, S., Krishnan, M., Smith, M.D.: Using Path Profiles to Predict HTTP Requests. In: Proceedings of the 7th International World Wide Web Conference, Brisbane, Australia (April 1998), http://www7.scu.edu.au/programme/fullpapers/1917/com1917.htm
Schwab, I., Kobsa, A., Koychev, I.: Learning about Users from Observation. In: Adaptive User Interfaces: Papers from the 2000 AAAI Spring Symposium, Menlo Park, CA, AAAI Press, Menlo Park (2000)
Shardanand, U., Maes, P.: Social Information Filtering: Algorithms for Automating Word of Mouth. In: Proceedings of the 1995 ACM Conference on Human Factors in Computing Systems (CHI’95), Denver, Colorado, pp. 210–217. ACM Press, New York (May 1995)
Sieg, A., Mobasher, B., Burke, R.: Inferring User’s Information Context from User Profiles and Concept Hierarchies. In: Proceedings of the 2004 Meeting of the International Federation of Classification Societies, IFCS 2004, Chicago, IL, pp. 563–574 (July 2004)
Silberschatz, A., Tuzhilin, A.: What makes patterns interesting in knowledge discovery systems. IEEE Transactions on Knowledge and Data Engineering 8(6), 970–974 (1996)
Sinha, R., Swearingen, K.: Comparing Recommendaions Made by Online Systems and Friends. In: Proceedings of Delos-NSF Workshop on Personalisation and Recommender Systems in Digital Libraries (June 2001)
Sinha, R., Swearingen, K.: The Role of Transaprency in Recommender Systems. In: CHI ’02 extended abstracts on Human factors in computing systems, pp. 830–831 (2002)
Smeaton, A., Murphy, N., O’Connor, N.E., Marlow, S., Lee, H., McDonald, K., Browne, P., Ye, J.: The físchlár digital video system: a digital library of broadcast TV programmes. In: Proceedings of the 1st ACM/IEEE-CS Joint Conference on Digital Libraries, Roanoke, Virginia, pp. 312–313. IEEE Computer Society Press, Los Alamitos (June 2001)
Smyth, P.: Probabilistic Model-based Clustering of Multivariate and Sequential Data. In: Heckerman, D., Whittaker, J. (eds.) Proceedings of the Seventh International Workshop on AI and Statistics, Los Gatos, CA, Morgan Kaufmann, San Francisco (Jan. 1999)
Spiliopoulou, M., Faulstich, L.: Wum: A tool for web utilization analysis. In: Atzeni, P., Mendelzon, A.O., Mecca, G. (eds.) Proceedings of EDBT Workshop at WebDB’98. LNCS, vol. 1590, pp. 184–203. Springer, Heidelberg (1999)
Spiliopoulou, M., Mobasher, B., Berendt, B., Nakagawa, M.: A Framework for the Evaluation of Session Reconstruction Heuristics in Web Usage Analysis. INFORMS Journal of Computing - Special Issue on Mining Web-Based Data for E-Business Applications 15(2) (2003)
Srivastava, J., Cooley, R., Deshpande, M., Tan, P.: Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data. SIGKDD Explorations 1(2), 12–23 (2000)
Steyvers, M., Smyth, P., Rosen-Zvi, M., Griffiths, T.: Probabilistic Author-Topic Models for Information Discovery. In: Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD’04), Seattle, Washington, pp. 306–315 (Aug. 2004)
Strehl, A., Ghosh, J.: Relationship-based Clustering and Visualization for High-dimensional Data Mining. INFORMS Journal Of Computing, Special Issue on Web Mining (A. Tuzhilin and L. Rashid, guest Eds.) 15(2), 208–230 (2003)
Suryavanshi, B.S., Shiri, N., Mudur, S.P.: Improving the Effectiveness of Model Based Recommender Systems for Highly Sparse and Noisy Web Usage Data. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence (WI’05), Compiegne, France, pp. 618–621. ACM Press, New York (Sept. 2005)
Swearingen, K., Sinha, R.: Beyond Algorithms: An HCI Perspective on Recommender Systems. In: Proceedings of the ACM SIGIR Workshop on Recommender Systems, New Orleans, LA. ACM Press, New York (Sept. 2001)
Tan, P., Kumar, V.: Discovery of Web Robot Sessions Based on Their Navigational Patterns. Data Mining and Knowledge Discovery 6, 9–35 (2002)
Tan, P., Kumar, V., Srivastava, J.: Selecting the right objective measure for association analysis. Information Systems 29(4), 293–313 (2004)
Tanasa, D., Trousse, B.: Advanced Data Preprocessing for Intersite Web Usage Mining. IEEE Intelligent Systems 19(2), 59–65 (2004)
Teevan, J., Dumais, S.T., Horvitz, E.: Personalizing Search Via Automated Analysis of Interests and Activities. In: Proceedings of 28th ACM SIGIR Conference on Research and Development in Information Retrieval, Salvador, Brazil, pp. 449–456. ACM Press, New York (Aug. 2005)
Trajkova, J., Gauch, S.: Improving Ontology-Based User Profiles. In: Proceedings of the Recherche d’Information Assiste par Ordinateur, RIAO 2004, University of Avignon (Vaucluse), France, pp. 380–389 (April 2004)
Ungar, L.H., Foster, D.P.: Clustering Methods For Collaborative Filtering. In: Proceedings of the AAAI98 Workshop on Recommendation Systems, Madison Wisconsin (July 1998)
Ypma, A., Heskes, T.: Categorization of Web Pages and User Clustering with Mixtures of Hidden Markov Models. In: Proceedings of the WEBKDD 2002 Workshop: Web Mining for Usage Patterns and User Profiles, at SIGKDD 2002, Edmonton, Alberta, Canada (July 2002)
Yu, K., Schwaighofer, A., Tresp, V., Ma, W., Zhang, H.: Collaborative Ensembling Learning: Combining Collaborative and Content-based Information Filtering. In: Proceedings of the 19th Conference on Uncertainty in Artificial Intelligence (UAI’03), Acapulco, Mexico, pp. 616–623 (Aug. 2003)
Yu, P.S.: Data Mining and Personalization Technologies. In: Proceedings of the International Conference on Database Systems for Advanced Applications (DASFAA99), Hsinchu, Taiwan, pp. 6–13 (April 1999)
Zhou, Y., Jin, X., Mobasher, B.: A Recommendation Model Based on Latent Principle Factors in Web Navigation Data. In: Proceedings of the 3rd International Workshop on Web Dynamics at WWW 2004 Conference, New York (2004)
Ziegler, C., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: Proceedings of the 14th international World Wide Web conference, Chiba, Japan, pp. 22–32 (May 2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this chapter
Cite this chapter
Mobasher, B. (2007). Data Mining for Web Personalization. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds) The Adaptive Web. Lecture Notes in Computer Science, vol 4321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72079-9_3
Download citation
DOI: https://doi.org/10.1007/978-3-540-72079-9_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72078-2
Online ISBN: 978-3-540-72079-9
eBook Packages: Computer ScienceComputer Science (R0)