Website browsing aid: A navigation graph-based recommendation system

Decision Support Systems

Volumn 45, Issue 3, 2008, Pages 387-400

  Wang, Youwei ^a   Dai, Weihui ^a   Yuan, Yufei ^b  

^a FUDAN UNIVERSITY   (China)

^b MCMASTER UNIVERSITY   (Canada)

Author keywords

Access log; Graph theory; Recommendation system; Website navigation aid

Indexed keywords

COMPUTER NETWORKS; COMPUTER SOFTWARE; GRAPH THEORY; INFORMATION SERVICES; INFORMATION SYSTEMS; INTERNET; MAPS; NAVIGATION; NAVIGATION CHARTS; SEARCH ENGINES;

(ALGORITHMIC) COMPLEXITY; DATA COLLECTED; ELSEVIER (CO); EXPERIMENTAL RESULTS; GRAPH-BASED; INFORMATION OVERLOADING; INTELLIGENT NAVIGATION; NAVIGATION PATTERNS; REAL WORLD; RECOMMENDATION ALGORITHMS; RECOMMENDATION SYSTEMS; RELEVANT INFORMATION; SELF-ADAPTIVE; WEB SITE;

WORLD WIDE WEB;

EID: 44849137581     PISSN: 01679236     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.dss.2007.05.006     Document Type: Article

References (41)

1. Anderson C., Domingos P., and Weld D. Adaptive Web Navigation for Wireless Devices. Proceedings of the 17th Joint Conference on Artificial Intelligence, Seattle, WA (2001) 879-884
2. Baeza-Yates R., and Ribeiro-Neto B. Mordern Information Retrieval (1999), Addison-Wesley
3. Balabanovic M., and Shoham Y. Fab: content-based, collaborative recommendation. Communications of the ACM 40 3 (1997) 66-72
4. Belkin N.J., and Croft W.B. Information filtering and information retrieval: two sides of the same coin?. Communications of the ACM 35 12 (1992) 29-38
5. Berghel H. Cyberspace 2000: dealing with information overload. Communications of the ACM 40 2 (1997) 19-24
6. Borges J., and Levene M. Mining association rules in hypertext databases. Proc. of the fourth International Conference on Knowledge Discovery and Data Mining, New York, USA (1998) 149-153
7. Burke R. Hybrid recommender systems: survey and experiments. User Modeling and User-Adapted Interaction 12 4 (2002) 331-370
8. Bunke H., and Messmer B. Recent advances in graph matching. International Journal of Pattern Recognition and Artificial Intelligence 11 1 (1997) 169-203
9. Bunke H., and Shearer K. A graph distance metric based on maximal common subgraph. Pattern Recognition Letters 19 3-4 (1998) 255-259
10. Chen M.S., Park J.S., and Yu P.S. Data mining for path traversal patterns in a Web environment. Proc. 16th IEEE International Conference on Distributed Computing Systems (1996) 385-392
11. Cooley R., Mobasher B., and Srivastava J. Data preparation for mining World Wide Web browsing patterns. Knowledge and Information Systems 1 1 (1999) 5-32
12. Dhyani D., Ng W.K., and Bhowmick S.S. A survey of web metrics. ACM Computing Surveys 34 4 (2002) 469-503
13. Fang X., and Liu Sheng O.R. LinkSelector: a web mining approach to hyperlink selection for web portals. ACM Transactions on Internet Technology 4 2 (2004) 209-237
14. S. Guha, R. Rastogi, K. Shim, CURE: an efficient clustering algorithm for large databases, Proc. 1998 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD'98), Seattle, WA, ACM Press, New York, USA, June 1998, pp. 73-84.
15. Guttman R.H., Moukas A.G., and Maes P. Agent-mediated electronic commerce: a survey. Knowledge Engineering Review 13 2 (1998) 147-159
16. Han J.W., and Kamber M. Data Mining: Concepts and Techniques (2001), Morgan Kaufmann Publishers
17. Herlocker J.L., Konstan J.A., Borchers A., and Riedl J. An Algorithmic Framework for Performing Collaborative Filtering. Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Berkeley, CA (1999) 230-237
18. Kilfoil M., Ghorbani A., Xing W., Lei Z., Lu J., Zhang J., and Xu X. Toward an adaptive web: the state of the art and science. Communication Networks and Services Research (CNSR) 2003 Conference, New Brunswick, CA (2003) 108-119 130
19. Kukulenz D., and Pauli J. Prediction of navigation profiles in a distributed Internet environment through learning of graph distributions. International Journal of Computational Intelligence and Applications vol. 2 (3) (2002), Imperial College Press 303-312
20. Lam W., and Mostafa J. Modeling user interest shift using a Bayesian approach. Journal of the American Society for Information Science and Technology 52 5 (2001) 416-429
21. Lebo H. The Digital Future Report: Surveying the Digital Future, Year Four, University of Southern California (Sep. 2004). http://www.digitalcenter.org/downloads/DigitalFutureReport-Year4-2004.pdf
22. Lieberman H. Letizia: an agent that assists web browsing. Proc. 14th Intl. joint Conf. on Artificial Intelligence (1995)
23. Mangiameli P., Chen S.K., and West D. A comparison of SOM neural network and hierarchical clustering methods. European Journal of Operational Research 93 (1996) 402-417
24. Mobasher B., Cooley R., and Srivastava J. Automatic personalization based on web usage. Communications of the ACM 43 8 (2001) 142-151
25. Nysveen H., and Pedersen P.E. An exploratory study of customers' perception of company web sites offering various interactive applications: moderating effects of customers' Internet experience. Decision Support Systems 37 (2004) 137-150
26. Perkins A. White paper: The classification of search engine spam (Sept. 2001). http://www.silverdisc.co.uk/articles/spam-classification/
27. M. Perkowitz, Adaptive Web Sites: Cluster Mining and Conceptual Clustering for Index Page Synthesis. PhD Thesis, Dept. of Comp. Sci. and Eng., Univ. of Washington, 2001.
28. Resnick P., and Varian H.R. Recommender Systems. Communications of the ACM 40 3 (1997) 56-58
29. Rich E. User modeling via stereotypes. Cognitive Science 3 (1979) 329-354
30. Schafer J.B., Konstan J., and Riedl J. Recommender Systems in e-Commerce. EC '99: Proceedings of the First ACM Conference on Electronic Commerce, Denver, CO (1999) 158-166
31. Smith K.A., and Ng A. Web page clustering using a self-organizing map of user navigation patterns. Decision Support Systems 35 (2003) 245-256
32. Spiliopoulou M., and Pohle C. Data mining to measure and improve the success of web sites. Data Mining and Knowledge Discovery 5 1-2 (2001) 85-114
33. Srikant R., and Yang Y. Mining Web Logs to Improve Web Site Organization. Proc. of World Wide Web (WWW01) (2001) 430-437
34. Toolan F., and Kushmetick N. Mining Web Logs for Personalized Site Maps. The Third International Conference on Web Information Systems Engineering Workshops (WISEw'02) Singapore (2002) 232-237
35. Towle B., and Quinn C. Knowledge based recommender systems using explicit user models. Knowledge-Based Electronic Markets, Papers from the AAAI Workshop, AAAI Technical Report WS-00-04 (2000), AAAI Press, Menlo Park, CA 74-77
36. Wallis W.D., Shoubridge P., Kraetz M., and Ray D. Graph distances using graph union. Pattern Recognition Letters 22 6-7 (2001) 701-704
37. Wang Y.W., Wang D.W., and Ip W.H. Optimal design of link structure for e-supermarket website. IEEE Transactions on Systems, Man and Cybernetics. Part A. Systems and Humans 36 2 (2006) 338-355
38. Ward J.H. Hierarchical grouping to optimize an objective function. Journal of American Statistical Association 58 301 (1963) 236-244
39. Yan T., Jacobsen M., Garcia-Molina H., and Dayal U. From User Access Patterns to Dynamic Hypertext Linking. Proceedings of the 5th International World Wide Web Conference, Paris, France (May 1996)
40. T. Zhang, R. Ramakrishnan, M. Livny, BIRCH: an efficient data clustering method for very large databases, Proc. 1996 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD'96), Montreal, Canada, ACM Press, New York, USA, June 1996, pp. 103-114.
41. Zhou Y., Wang Y.W., Zhang Y.Y., and Dai W.H. Hyperlink Structure-based Recommender System. The Eighth Pacific-Asia Conference on Information Systems (PACIS2004) (8-11 July 2004). http://www.pacis-net.org Shanghai, China, http://www.pacis-net.org/