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Personalisation in web computing and informatics: Theories, techniques, applications, and future research

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Abstract

Recently, personalised search engines and recommendation systems have been widely adopted by users who require assistance in searching, classifying, and filtering information. This paper presents an overview of the field of personalisation systems and describes current state-of-the-art methods and techniques. It reviews approaches for (1) user profiling, including behaviour, preference, and intention modelling; (2) content modelling, comprising content representation, analysis, and classification; and (3) filtering methods for recommendation, classified into four main categories: rule-based, content-based, collaborative, and hybrid filtering. The paper also discusses personalisation systems in different domains, and various techniques and their limitations. Finally, it identifies several issues and possible directions for further research that can improve recommendation capabilities and enhance personalised systems.

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References

  • Adomavicius, G. & Kwon, Y. O. (2007) New recommendation techniques for multicriteria rating systems. IEEE Intelligent Systems, 48–55.

  • Adomavicius, G., & Tuzhilin, A. (2001). Multidimensional recommender systems: a data warehousing approach. Lecture Notes in Computer Science: Proceedings of the Second International Workshop on Electronic Commerce. 2232.

    Google Scholar 

  • Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. Knowledge and Data Engineering, IEEE Transactions on, 17, 734–749.

    Article  Google Scholar 

  • Adomavicius, G., Sankaranarayanan, R., Sen, S., & Tuzhilin, A. (2005). Incorporating contextual information in recommender systems using a multidimensional approach. ACM Transactions on Information Systems (TOIS), 23, 103–145.

    Article  Google Scholar 

  • Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. ACM SIGMOD Record, 22, 207–216.

    Article  Google Scholar 

  • Albanese, M., Picariello, A., Sansone, C. & Sansone, L. (2004) Web personalization based on static information and dynamic user behavior. Proceedings of the 6th annual ACM international workshop on Web information and data management, 80–87.

  • Alsabti, K., Ranka, S. & Singh, V. (1998) An efficient k-means clustering algorithm. First Workshop on High-Performance Data Mining, 10.

  • Arthur, D. & Vassilvitskii, S. (2006) How slow is the k-means method? Proceedings of the twenty-second annual symposium on Computational geometry, 144–153.

  • Balabanovic, M., & Shoham, Y. (1997). Fab: content-based, collaborative recommendation. Communications of the ACM, 40, 66–72.

    Article  Google Scholar 

  • Berners-Lee, T., Hendler, J., & Lassila, O. (2001). The semantic web. Scientific American, 284, 28–37.

    Article  Google Scholar 

  • Billsus, D. & Pazzani, M. J. (1999) A hybrid user model for news story classification. Proceedings of the seventh international conference on User modeling table of contents, 99–108.

  • Blanco-Fernandez, Y., Pazos-Arias, J. J., Gil-Solla, A., Ramos-Cabrer, M., Lopez-Nores, M., Garcia-Duque, J., et al. (2008). A flexible semantic inference methodology to reason about user preferences in knowledge-based recommender systems. Knowledge-Based Systems, 21, 305–320.

    Article  Google Scholar 

  • Blei, D. M., Ng, A. Y., Jordan, M. I., & Lafferty, J. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.

    Article  Google Scholar 

  • Bomhardt, C. (2004) NewsRec, a SVM-driven personal recommendation system for news websites. Web Intelligence, 2004. Proceedings. IEEE/WIC/ACM International Conference on.

  • Breese, J. S., Heckerman, D., & Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. Learning, 9, 309–347.

    Google Scholar 

  • Brin, S., Motwani, R. & Silverstein, C. (1997) Beyond market baskets: generalizing association rules to correlations. Proceedings of the 1997 ACM SIGMOD international conference on Management of data, 265–276.

  • Callan, J. & Smeaton, A. (2003) Personalization and recommender systems in digital libraries. Joint NSF-EU DELOS Working Group Report, available at: www.dli2.nsf.gov/internationalprojects/working_group_reports/personalisation.html.

  • Carchiolo, V., Longheu, A., Malgeri, M. & Mangioni, G. (2003) Courses personalization in an e-learning environment. Advanced Learning Technologies, 2003. Proceedings. The 3 rd IEEE International Conference on.

  • Chedrawy, Z. & Abidi, S. S. R. (2006) Case based reasoning for information personalization: using a context-sensitive compositional case adaptation approach. Engineering of Intelligent Systems, 2006 IEEE International Conference on.

  • Chen, C. M., & Duh, L. J. (2008). Personalized web-based tutoring system based on fuzzy item response theory. Expert Systems With Applications, 34, 2298–2315.

    Article  Google Scholar 

  • Chen, Q., & Norcio, A. F. (1997). Modeling a user’s domain knowledge with neural networks. International Journal of Human-Computer Interaction, 9, 25–40.

    Article  Google Scholar 

  • Chen, Z., Lin, F., Liu, H., Liu, Y., Ma, W. Y., & Wenyin, L. (2002). User intention modeling in web applications using data mining. World Wide Web, 5, 181–191.

    Article  Google Scholar 

  • Cheong, S. N., Kam, H. S., Azhar, K. M. & Hanmandlu, M. (2002) A web-based collaborative enabled multimedia content authoring and management system for interactive and personalized online learning. Computers in Education, 2002. Proceedings. International Conference on, 872–873.

  • Chesnais, P. R., Mucklo, M. J. & Sheena, J. A. (1995) The Fishwrap personalized news system. Community Networking, 1995.‘Integrated Multimedia Services to the Home’., Proceedings of the Second International Workshop on, 275–282.

  • Cheung, K. W., Kwok, J. T., Law, M. H., & Tsui, K. C. (2003). Mining customer product ratings for personalized marketing. Decision Support Systems, 35, 231–243.

    Article  Google Scholar 

  • Chiu, W. (2001) Web site personalization.

  • Cho, Y. H., Kim, J. K., & Kim, S. H. (2002). A personalized recommender system based on web usage mining and decision tree induction. Expert Systems With Applications, 23, 329–342.

    Article  Google Scholar 

  • Choi, O., & Han, S. Y. (2008). Personalization of rule-based web services. Sensors, 8, 2424–2435.

    Article  Google Scholar 

  • Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D. & Sartin, M. (1999) Combining content-based and collaborative filters in an online newspaper. ACM SIGIR Workshop on Recommender Systems.

  • Conlan, O., Wade, V., Bruen, C. & Gargan, M. (2002) Multi-model, metadata driven approach to adaptive hypermedia services for personalized elearning. Proceedings of the Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, 100–111.

  • Dahn, I. & Schwabe, G. (2002) Personalizing textbooks with slicing technologies-concept, tools, architecture, collaboration use. Proceedings of the 35th Annual Hawaii International Conference on System Sciences (HICSS’02)-Volume.

  • Das, A. S., Datar, M., Garg, A. & Rajaram, S. (2007) Google news personalization: scalable online collaborative filtering. Proceedings of the 16th international conference on World Wide Web, 271–280.

  • De Moor, A. (2005) Patterns for the pragmatic web. Conceptual Structures: Common Semantics for Sharing Knowledge: 13th International Conference on Conceptual Structures. Kassel, Germany.

  • De Moor, A., KEELER, M. & RICHMOND, G. (2002) Towards a pragmatic web. Conceptual Structures: Integration and Interfaces: 10th International Conference on Conceptual Structures, ICCS 2002, Borovets, Bulgaria, July 15–19, 2002: Proceedings.

  • Di Giacomo, M., Mahoney, D., Bollen, J., Monroy-Hernandez, A. & Ruiz Meraz, C. M. (2001) MyLibrary, a personalization service for digital library environments. Proceedings of the Second DELOS Network of Excellence Workshop on Personalisation and Recommender Systems in Digital Libraries (= ERCIM Workshop Proceedings 01/W03). Dublin.

  • Eirinaki, M., & Vazirgiannis, M. (2003). Web mining for web personalization. ACM Transactions on Internet Technology, 3, 1–27.

    Article  Google Scholar 

  • Fink, J., & Kobsa, A. (2000). A Review and Analysis of Commercial User Modeling Servers for Personalization on the World Wide Web. User Modeling and User-Adapted Interaction, 10, 209–249.

    Article  Google Scholar 

  • Ford, N. & Chen, S. Y. (2000) Individual differences, hypermedia navigation, and learning: an empirical study. Journal of Educational Multimedia and Hypermedia, 9.

  • Frias-Martinez, E., Magoulas, G., Chen, S., & Macredie, R. (2006). Automated user modeling for personalized digital libraries. International Journal of Information Management, 26, 234–248.

    Article  Google Scholar 

  • Friedman, N., Geiger, D., & Goldszmidt, M. (1997). Bayesian network classifiers. Machine Learning, 29, 131–163.

    Article  Google Scholar 

  • Gamerman, D. & Lopes, H. F. (2006) Markov chain Monte Carlo: stochastic simulation for Bayesian inference, Chapman & Hall/CRC.

  • Gao, M. & Zhongfu, W. (2009) Incorporating pragmatic information in personalized recommendation systems. The 11th International Conference on Informatics and Semiotics in Organisations. 156–164.

  • Gao, M., Zhongfu, W. & Liu, K. (2008) Pragmatic Grid for personalized resource provision. Service Operations and Logistics, and Informatics, 2008. IEEE/SOLI 2008. IEEE International Conference on.

  • Gauch, S. (2003). Ontology-based personalized search and browsing. Web Intelligence and Agent Systems, 1, 219–234.

    Google Scholar 

  • Ha, S. H. (2002) Helping online customers decide through web personalizatio. IEEE INTELLIGENT SYSTEMS, 34–43.

  • Haddawy, P. (1994) Generating Bayesian networks from probability logic knowledge bases. Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, 262–269.

  • Han, J. & Kamber, M. (2006) Data mining: concepts and techniques, Morgan Kaufmann.

  • Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: a k-means clustering algorithm. Applied Statistics, 28, 100–108.

    Article  Google Scholar 

  • Hipp, J., Guntzer, U. & Nakhaeizadeh, G. (2002) Data mining of association rules and the process of knowledge discovery in databases. Industrial Conference on Data Mining, 15–26.

  • Hofmann, T. (1999) Probabilistic latent semantic analysis. matrix, 50, 2.

  • Hofmann, T. (2003) Collaborative filtering via gaussian probabilistic latent semantic analysis. Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, 259–266.

  • Hofmann, T. (2004). Latent semantic models for collaborative filtering. ACM Transactions on Information Systems (TOIS), 22, 89–115.

    Article  Google Scholar 

  • Horvitz, E., Breese, J., Heckerman, D., Hovel, D. & Rommelse, K. (1998) The Lumiere project: bayesian user modeling for inferring the goals and needs of software users. Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, 256–265.

  • Hsu, H. H., Chen, C. J. & Tai, W. P. (2003) Towards error-free and personalized Web-based courses. Advanced Information Networking and Applications, 2003. AINA 2003. 17th International Conference on, 99–104.

  • Im, K. H., & Park, S. C. (2007). Case-based reasoning and neural network based expert system for personalization. Expert Systems with Applications, 32, 77–85.

    Article  Google Scholar 

  • Instone, K. (2000) Information architecture and personalization. White Paper, Argus Associates, INC., Dec.

  • Isozaki, H. & Kazawa, H. (2002) Efficient support vector classifiers for named entity recognition. Proceedings of the 19th international conference on Computational linguistics-Volume 1, 1–7.

  • Jayawardana, C., Hewagamage, K. P., & Hirakawa, M. (2001). Personalization tools for active learning in digital libraries. MC Journal: The Journal of Academic Media Librarianship, 8, 1.

    Google Scholar 

  • Jeevan, V. K. J. & Padhi, P. (2006) A selective review of research in content personalization Library Review, Volume 55, Number 9, 2006 , pp. 556–586(31), 55, 556–586.

  • Jin, X., Zhou, Y. & Mobasher, B. (2004) Web usage mining based on probabilistic latent semantic analysis. Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, 197–205.

  • Joachims, T. (1997) A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization. Proceedings of the Fourteenth International Conference on Machine Learning, 143–151.

  • Joachims, T., Nedellec, C. & Rouveirol, C. (1998) Text categorization with support vector machines: learning with many relevant. Springer.

  • Jokela, S., Turpeinen, M., Kurki, T., Savia, E. & Sulonen, R. (2001) The role of structured content in a personalized news service. System Sciences, 2001. Proceedings of the 34th Annual Hawaii International Conference on.

  • Jung, S. Y., Hong, J. H. & Kim, T. S. (2005) A statistical model for user preference. IEEE Transactions on Knowledge and Data Engineering, 834–843.

  • Karagiannidis, C., Sampson, D. & Cardinali, F. (2001) An architecture for defining re-usable adaptive educational content. Proceedings of the 2nd International Conference on Advanced Learning Technologies, 21–24.

  • Kim, J. K., Cho, Y. H., Kim, W. J., Kim, J. R., & Suh, J. H. (2002). A personalized recommendation procedure for Internet shopping support. Electronic Commerce Research and Applications, 1, 301–313.

    Article  Google Scholar 

  • Kim, J. W. (2001). Application of decision-tree induction techniques to personalized advertisements on internet storefronts. International Journal of Electronic Commerce, 5, 45–62.

    Google Scholar 

  • Kitts, B., Freed, D. & Vrieze, M. (2000) Cross-sell: a fast promotion-tunable customer-item recommendation method based on conditionally independent probabilities. Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, 437–446.

  • Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R., & Riedl, J. (1997). GroupLens: applying collaborative filtering to Usenet news. Communications of the ACM, 40, 77–87.

    Article  Google Scholar 

  • Kramer, J., Noronha, S., & Vergo, J. (2000). A user-centered design approach to personalization. Communications of the ACM, 43, 44–48.

    Article  Google Scholar 

  • Kuo, Y. F., & Chen, L. S. (2001). Personalization technology application to Internet content provider. Expert Systems with Applications, 21, 203–215.

    Article  Google Scholar 

  • Lai, H. J., Liang, T. P., & Ku, Y. C. (2003). Customized Internet news services based on customer profiles. Proceedings of the 5th international conference on Electronic commerce, 225–229.

  • Li, Y., Lu, L., & Xuefeng, L. (2005). A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in E-Commerce. Expert Systems With Applications, 28, 67–77.

    Article  Google Scholar 

  • Liang, T.-P. & Lai, H.-J. (2002) Discovering user interests from Web browsing behavior: an application to Internet news services. System Sciences, 2002. HICSS. Proceedings of the 35th Annual Hawaii International Conference on.

  • Liang, T.-P., Yang, Y.-F., Chen, D.-N. & Ku, Y.-C. (2007) A semantic-expansion approach to personalized knowledge recommendation. Decision Support Systems, In Press, Corrected Proof.

  • Linden, G., Smith, B., & York, J. (2003). Amazon. com recommendations: item-to-item collaborative filtering. IEEE Internet computing, 7, 76–80.

    Article  Google Scholar 

  • Liu, B., Hsu, W. & Ma, Y. (1998) Integrating classification and association rule mining. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, 80–86.

  • Liu, D. R., & Shih, Y. Y. (2005). Hybrid approaches to product recommendation based on customer lifetime value and purchase preferences. The Journal of Systems & Software, 77, 181–191.

    Article  Google Scholar 

  • Liu, F., Yu, C., & Meng, W. (2004). Personalized web search for improving retrieval effectiveness. IEEE Transactions on Knowledge and Data Engineering, 16, 28–40.

    Article  Google Scholar 

  • Liu, K. (2000) Semiotics in information systems engineering, Cambridge University Press.

  • Magoulas, G. D., Papanikolaou, & Grigoriadou, M. (2003). Adaptive web-based learning: accommodating individual differences through system’s adaptation. British Journal of Educational Technology, 34, 511–527.

    Article  Google Scholar 

  • Masters, J., Madhyastha, T., & Shakouri, A. (2008). ExplaNet: A Collaborative Learning Tool and Hybrid Recommender System for Student-Authored Explanations. Journal of Interactive Learning Research, 19, 51–74.

    Google Scholar 

  • Melville, P., Mooney, R. J. & Nagarajan, R. (2002) Content-Boosted collaborative filtering for improved recommendations. Proceedings of the Eighteenth National Conference on Artificial Intelligence, 187–192.

  • Min, S. H., & Han, I. (2005). Detection of the customer time-variant pattern for improving recommender systems. Expert Systems with Applications, 28, 189–199.

    Article  Google Scholar 

  • Mobasher, B. (2007) Data mining for web personalization. The Adaptive Web: Methods and Strategies of Web Personalization, Brusilovsky, P., Berlin Heidelberg New York: Springer Verlag.

  • Mock, K. J., & Vemuri, V. R. (1997). Information filtering via hill climbing, WordNet, and index patterns. Information Processing and Management, 33, 633–644.

    Article  Google Scholar 

  • Montaner, M., Lopez, B., & DELA Rosa, J. L. (2003). A taxonomy of recommender agents on the internet. Artificial Intelligence Review, 19, 285–330.

    Article  Google Scholar 

  • Montgomery, A. & Smith, M. D. (2008) Prospects for personalization on the Internet, SSRN.

  • Mooney, R. J. & Roy, L. (2000) Content-based book recommending using learning for text categorization. Proceedings of the fifth ACM conference on Digital libraries, 195–204.

  • Morgan, E. L. (2002) Making information easier to find with MyLibrary, Infomotions, Inc.

  • Murthi, B. P. S., & Sarkar, S. (2003). The role of the management sciences in research on personalization. Management Science, 49, 1344–1362.

    Article  Google Scholar 

  • Nanopoulos, A., Katsaros, D., & Manolopoulos, Y. (2001). Effective prediction of web-user accesses: a data mining approach. WEBKDD, 1, 2001.

    Google Scholar 

  • Narayanan, S., Bailey, W., Tendulkar, J., & Daley, R. (2002). Design of model-based interfaces for a real world informationsystem. Systems, Man and Cybernetics, Part A, IEEE Transactions on, 32, 11–24.

    Article  Google Scholar 

  • Nasraoui, O. (2005) World Wide Web personalization. Encyclopedia of Data Mining and Data Warehousing, Idea Group.

  • Niu, L., Yan, X. W., Zhang, C. Q. & Zhang, S. C. (2002) Product hierarchy-based customer profiles for electronic commerce recommendation. Machine Learning and Cybernetics, 2002. Proceedings. 2002 International Conference on, 2.

  • Nuel, G. (2006). Pattern statistics on Markov chains and sensitivity to parameter estimation. Algorithms for Molecular Biology, 1, 17.

    Article  Google Scholar 

  • Papadimitriou, C. H., Raghavan, P., Tamaki, H., & Vempala, S. (2000). Latent semantic indexing: a probabilistic analysis. Journal of Computer and System Sciences, 61, 217–235.

    Article  Google Scholar 

  • Papanikolaou, K. A. & Grigoriadou, M. (2003) An instructional framework supporting personalized learning on the Web. Advanced Learning Technologies, 2003. Proceedings. The 3 rd IEEE International Conference on, 120–124.

  • Park, Y.-J. & Chang, K.-N. (2008) Individual and group behavior-based customer profile model for personalized product recommendation. Expert Systems with Applications, In Press, Corrected Proof.

  • Pazzani, M. J. & Billsus, D. (2006) Content-based recommendation systems. The Adaptive Web: Methods and Strategies of Web Personalization, Lecture Notes in Computer Science, 4321.

  • Perugini, S., & Ramakrishnan, N. (2003). Personalizing web sites with mixed-initiative interaction. IT Professional, 5, 9–15.

    Article  Google Scholar 

  • Piatetsky-Shapiro, G. (1991) Discovery, analysis and presentation of strong rules. Knowledge Discovery in Databases, G. Piatetsky-Shapiro and W. Frawley editors, MIT Press, Cambridge, MA.

  • Pretschner, A. & Gauch, S. (1999) Ontology based personalized search. Tools with Artificial Intelligence, 1999. Proceedings. 11th IEEE International Conference on, 391–398.

  • Quarati, A. (2003) Designing shareable and personalisable e-learning paths. Information Technology: Coding and Computing [Computers and Communications], 2003. Proceedings. ITCC 2003. International Conference on, 454–460.

  • Ramakrishnan, N., Keller, B. J., Mirza, B. J., Grama, A. Y., & Karypis, G. (2001). Privacy risks in recommender systems. IEEE Internet Computing, 5, 54–62.

    Article  Google Scholar 

  • Renda, M. E., & Straccia, U. (2005). A personalized collaborative Digital Library environment: a model and an application. Information Processing and Management, 41, 5–21.

    Article  Google Scholar 

  • Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P. & Riedl, J. (1994) GroupLens: an open architecture for collaborative filtering of netnews. Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, 175–186.

  • Riecken, D. (2000). Introduction: personalized views of personalization. Communications of the ACM, 43, 26–28.

    Article  Google Scholar 

  • Ruvini, J. D. (2003) Adapting to the user’s internet search strategy. Proceedings of the 9th International Conference on User Modeling (UM2003), Pittsburgh, 55–64.

  • Sakagami, H., & Kamba, T. (1997). Learning personal preferences on online newspaper articles from user behaviors. Computer Networks and ISDN Systems, 29, 1447–1455.

    Article  Google Scholar 

  • Sarukkai, R. R. (2000). Link prediction and path analysis using Markov chains. Computer Networks, 33, 377–386.

    Article  Google Scholar 

  • Schein, A. I., Popescul, A., Ungar, L. H. & Pennock, D. M. (2002) Methods and metrics for cold-start recommendations. Proceedings of the 25th annual international ACM SIGIR conference on Research and Development in Information Retrieval. ACM New York, NY, USA.

  • Schmitt, B. & Oberlander, S. (2002) Evaluating and enhancing meta-search performance in digital libraries. Web Information Systems Engineering, 2002. WISE 2002. Proceedings of the Third International Conference on, 93–102.

  • Schubert, P. & Koch, M. (2002) The power of personalization: customer collaboration and virtual communities. Proceedings of the Eighth Americas Conference on Information Systems (AMCIS), 1953–1965.

  • Scime, A. (1997) Taxonomic information retrieval (TAXIR) from the World Wide Web: knowledge-based query and results refinement with user profiles and decision models. George Mason University.

  • Shepherd, M., Watters, C. & Marath, A. T. (2002) Adaptive user modeling for filtering electronic news. System Sciences, 2002. HICSS. Proceedings of the 35th Annual Hawaii International Conference on, 1180–1188.

  • Shin, C.-K., Yun, U. T., Kim, H. K., & Park, S. C. (2000). A hybrid approach of neural network and memory-based learning to data mining. Neural Networks, IEEE Transactions on, 11, 637–646.

    Article  Google Scholar 

  • Singh, M. P. (2002). The pragmatic web: preliminary thoughts. Amicalola Falls State Park, GA: NSF-OntoWeb Workshop on Database and Information Systems Research for Semantic Web and Enterprises.

    Google Scholar 

  • Sun, X., Chen, Z., Liu, W. & Ma, W. Y. (2002) Intention modeling for web navigation. Proceedings of the 11th World Wide Web Conference (WWW).

  • Tang, C., Lau, R. W. H., Li, Q., Yin, H., Li, T. & Kilis, D. (2000) Personalized courseware construction based on web data mining. Proceedings of the International Conference on Web Information Systems Engineering, 204–211.

  • Teknomo, K. (2008) K-Means clustering tutorials. http://people.revoledu.com/kardi/tutorial/kMean/.

  • Tong, S., & Koller, D. (2001). Active learning for structure in Bayesian networks. Proceedings of the International Joint Conference on Artificial Intelligence, 17, 863–869.

    Google Scholar 

  • Ubois, J. (1996). Cast system. Internet World, 7(94–98), 100.

    Google Scholar 

  • Wallace, M., Karpouzis, K., Stamou, G., Moschovitis, G., Kollias, S. & Schizas, C. (2003) The electronic road: personalized content browsing. IEEE MULTIMEDIA, 49–59.

  • Wang, G. T., Xie, F., Tsunoda, F., Maezawa, H. & Onoma, A. K. (2002) Web search with personalization and knowledge. Multimedia Software Engineering, 2002. Proceedings. Fourth International Symposium on.

  • Wei, C. P., Yang, C. S., & Hsiao, H. W. (2008). A collaborative filtering-based approach to personalized document clustering. Decision Support Systems, 45, 413–428.

    Article  Google Scholar 

  • Weller, M. (2007). Virtual learning environments: using, choosing and developing your VLE. Oxon: Routledge.

    Google Scholar 

  • Witten, I. H., & Frank, E. (2002). Data mining: practical machine learning tools and techniques with Java implementations. ACM SIGMOD Record, 31, 76–77.

    Article  Google Scholar 

  • Wu, K. L., Aggarwal, C. C. & Yu, P. S. (2001) Personalization with dynamic profiler. Proceedings of the third International Workshop on Advanced Issues of E-Commerce and Web-Based Information Systems.

  • Xu, C. Z. & Ibrahim, T. I. (2004) A keyword-based semantic prefetching approach in internet news services. IEEE Transactions on Knowledge and Data Engineering, 601–611.

  • Yang, Y. (1999). An evaluation of statistical approaches to text categorization. Information Retrieval, 1, 69–90.

    Article  Google Scholar 

  • Yang, Y. & Webb, G. I. (2002) A comparative study of discretization methods for naive-bayes classifiers. Proceedings of PKAW, 159–173.

  • Youm, S. H. & Cho, D. S. (2001) Personalized recommendation based on item dependency map. Industrial Electronics, 2001. Proceedings. ISIE 2001. IEEE International Symposium on, 1.

  • Yu, K., Schwaighofer, A., Tresp, V., Xu, X. & Kriegel, H. P. (2004) Probabilistic memory-based collaborative filtering. IEEE Transactions on Knowledge and Data Engineering, 56–69.

  • Zhang, Z. (2007) Community— the dawn of personalisation technology in the next generation Internet. http://googlechinablog.com/2007/08/blog-post_20.html 2008.

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Acknowledgments

We gratefully acknowledge valuable input from Dr. Lily Sun, Director of Postgraduate Studies, and Wenge Rong, PhD student, both from the School of Systems Engineering, the University of Reading. Thanks are also due to Hubert Grzybek from the Informatics Research Centre for his help in polishing the use of English.

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  1. College of Computer Science, Chongqing University, Chongqing, 400044, China

    Min Gao

  2. Informatics Research Centre, University of Reading, Reading, RG66WB, UK

    Kecheng Liu

  3. College of Computer Science, Chongqing University, Chongqing, 400044, China

    Zhongfu Wu

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  1. Min Gao
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Correspondence to Min Gao.

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This work was supported by the National Social Sciences Foundation of China under Grant No. ACA07004-08, Postdoctoral Science Foundation of China under Grant No. 20080440699, Projects of Chongqing under Grant No. 2008BB2183, KJ071601 and 2008-ZJ-064, conducted while M. Gao was visiting scholar at the University of Reading from Nov. 2007 to Nov. 2008, sponsored by China Scholarship Council

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Gao, M., Liu, K. & Wu, Z. Personalisation in web computing and informatics: Theories, techniques, applications, and future research. Inf Syst Front 12, 607–629 (2010). https://doi.org/10.1007/s10796-009-9199-3

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