|
[1] C. Aggarwal and P. Yu, “Data Mining Techniques for Personalization,” IEEE Data Engineering Bulletin, Vol. 23, No. 1, pp. 4-9, 2000. [2] B. Amento, L. Terveen, and W. Hill, “Does Authority Mean Quality? Predicting Expert Quality Ratings of Web Documents,” In Proceedings of 23th International ACM SIGIR, pp. 296-303, 2000. [3] M. Angelaccio and B. Buttarazzi, “Local Searching the Internet,” IEEE Internet Computing, Vol. 6, No. 1, pp. 25-33, 2002. [4] A. Arasu, J. Cho, H. Garcia-Molina, A. Paepcke, and S. Raghavan, “Searching the Web,” ACM Transactions on Internet Technology, Vol. 1, No. 1, pp. 97-101, 2001. [5] R. Baeza-Yates and B. Riberiro-Neto, Modern Information Retrieval, Addison-Wesley, 1999. [6] M. Balabanovic and Y. Shoham, “Fab: Content-based, Collaborative Recommendation,” Communications of the ACM, Vol. 40, No. 3, pp. 66-72, March 1997. [7] A. Banerjee and J. Ghosh, “Concept-based Clustering of Clickstream Data,” In Proceedings of 3rd International Conference on Information Technology, Bhubaneshwar, pp. 145-150, 2000. [8] D. Beeferman, and A. Berger, “Agglomerative clustering of a search engine query log,” Knowledge Discovery and Data Mining, pp. 406- 416, 2000. [9] H. Berghel, “Cyberspace 2000: Dealing with Information overload,” Communications of the ACM, Vol. 40, No. 2, pp. 19-24, Feb. 1997. [10] K. Bharat and G..A. Mihaila, “When Experts Agree: Using Non-Affiliated Experts to Rank Popular Topics,” ACM Transactions on Information Systems, Vol. 20, No. 1, pp. 47-58, 2002. [11] A. Borchers, J. Herlocker, J. Konstanand, and J. Riedl, “Ganging up on information overload,” Computer, Vol. 31, No. 4, pp. 106-108, Apr. 1998. [12] S. Brin and L. Page, “The Anatomy of a Large-Scale Hypertextual Web Search Engine,” Computer Networks and ISDN Systems, Vol. 30, pp. 107-117, 1998. [13] S. Brin and L. Page, “The PageRank Citation Ranking: Bringing Order to the Web,” In Proceedings of ASIS’98, pp. 161-172, 1998. [14] J. Budzik, K. J. Hammond, L. Birnbaum, and M. Krema, ”Beyond Similarity,” Proceedings of the 2000 Workshop on Artificial Intelligence and Web Search, AAAI Press, 2000. [15] J.P. Callan, Z. Lu, W.B. Croft, “Searching distributed collections with inference networks,” In Proceeding of the ACM SIGIR Conference, pp. 21-28, Seattle (July 1995). [16] S. Chakrabarti, “Data Mining for Hypertext: A Tutorial Survey,” ACM SIGKDD Explorations, Vol. 1, No. 2, pp. 1-11, 2000. [17] S. Chakrabarti, B.E. Dom, S. R. Kumar, P. Raghavan, S. Rajagopalan, A. Tomkins, D. Gibson, and J. Kleinberg, “Mining the Web's Link Structure,” IEEE Computer, Vol. 32, pp. 60-67, 1999. [18] P. Chan, “Constructing Web User Profiles: A Non-invasive Learning Approach,” In Web Usage Analysis and User Profiling, LNAI 1836, pp. 39-55, 2000. [19] C. H. Chang and C. C. Hsu, “Enabling Concept-Based Relevance Feedback for Information Retrieval on the WWW,” IEEE Transactions on Knowledge and Data Engineering, Vol. 11, No. 4, pp. 595-609, July/August 1999. [20] C. H. Chi, C. Ding, and K. Y. Lam, “Study For Fusion Of Different Sources To Determine Relevance,” Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence (ICTAI’02), 2002. [21] B. Chidlovskii, N. Glance, and A. Grasso, “Collaborative Re-Ranking of Search Results,” In Proceedings of AAAI-2000 Workshop on Artificial Intelligence for Web Search, Austin, Texas, pp. 18-22, 2000. [22] M. Claypool, D. Brown, P. Le, and M. Waseda, “Inferring User Interest,” IEEE Internet Computing, Vol. 5, No. 6, pp. 32-39, Nov.-Dec. 2001. [23] J. Conklin, “Hypertext: An introduction and survey,” Computer, Vol. 20, No. 9, pp. 17- 41, 1987. [24] G. Culliss, “User Popularity Ranked Search Engines Gary Culliss Chairman and Cofounder Direct Hit Technologies,” April 1999, available at: http://www.infonortics.com/searchengines/boston1999/culliss/ [25] J. Dean and M.R. Henzinger, “Finding Related Pages in World Wide Web,” In Proceedings of the 8th International World Wide Web Conference, pp. 389-401, 1999. [26] D. Dreilinger and A.E. Howe, “Experience with selecting search engine using metasearch,” ACM Transaction on Information System, Vol. 15, No. 3, pp. 195-222, (July 1997). [27] L. Finkelstenin, E. Gabrilovich, Y. Matias, and E. Ruppin, G. Wolfman, and E. Ruppin, “Placing Search in Context: The Concept Revisited,” ACM Transactions on Information Systems, Vol. 20, No. 1, pp. 116-131, 2002. [28] A. Garratt, M. Jackson, P. Burden, and J. Wallis, “A Survey of Alternative for A Search Engine Storage Structure,” Information and Software Technology, Vol. 43, No. 11, pp. 661-677, 2001. [29] E. J. Glover, S. Lawrence, W. P. Birmingham, and C. L. Giles, “Architecture of a Metasearch Engine that Supports User Information Needs,” Proceedings of the 8th International Conference on Information Knowledge Management, pp. 210-216, 1999. [30] N. Good, J. Schafer, J. Konstan, A. Borchers, and B. Sarwer, “Combining Collaborative Filtering with Personal Agents for Better Recommendations,” In Proceedings of the American Association of Artificial Intelligence AAAI-99, pp. 439-446, 1999. [31] C. Gurrin and A.F. Smeaton, “A Connectivity Analysis Approach to Increasing Precision in Retrieval from Hyperlinked Documents,” In Proceedings of TREC8, Washington DC, pp. 357-366, 1999. [32] E. Gutman, “Innovative Search Methods,” CIS 732 Research Paper, 2000. [33] J. Han and C.C. Chang, “Data mining for web intelligence,” Computer, Vol. 35, No. 11, pp. 64 -70, 2002. [34] J. Han and M. Kamber, Data Mining Concept and Techniques, Morgan Kaufmann, ISBN 1-55860-489-8, 2000. [35] M.R. Herzinger, “Hyperlink Analysis for the Web,” IEEE Internet Computing, Vol. 5, No.1, pp. 45-50, 2001. [36] L. Huang, “A Survey on Web Information Retrieval Technologies,” Working Paper, http://citeseer.nj.nec.com/336617.html. [37] B. J. Jansen, A. Spink, and T. Saracevic, “Real life, real users, and real needs: A study and analysis of user queries on the web,” Information Processing and Management, Vol. 36, No. 2, pp. 207-227, 2000. [38] M. F. Jiang, S. S. Tseng and Y. T. Lin, “Collaborative Rating System for Web Page Labeling,” World Conference of the WWW and Internet, Honolulu, Hawaii, USA, Vol. 1, pp. 569-574, 1999. [39] T. Joachims, ”Optimizing Search Engines Using Clickthrough Data,” Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD), 2002. [40] L. Kerschberg, W. Kim, and A. Scime, “Intelligent Web Search via Personalizable Meta-Search Agents,” International Conference on Ontologies, Databases and Applications of Semantics (ODBASE 2002), (Irvine, CA, 2002). [41] K. J. Kim and S. B. Cho, “A Personalized Web Search Engine Using Fuzzy Concept Network with Link Structure,” IFSA World Congress and 20th NAFIPS International Conference, Vol. 1, pp. 81-86, 2001. [42] J. Kleinberg, “Authoritative sources in a hyperlinked environment,” In Proceedings of 9th ACM-SIAM Symposium on Discrete Algorithms, pp. 668-679, 1998. [43] M. Kobayashi and K. Takeda, “Information Retrieval on the Web,” ACM Computing Surveys, Vol. 32, No. 2, pp. 144-173, 2000. [44] J. A. Konstan, B. N. Miller, D. Maltz, J. L. Herlocker, L. R. Gordon, and J. Riedl, “GroupLens: Applying Collaborative Filtering to Usenet News,” Communications of ACM, Vol. 40, No. 3, pp. 77-87, March 1997. [45] R. Kosala and H. Blockeel, “Web Mining Research: A Survey,” ACM of SIGKDD Explorations, Vol. 2, No.1, pp. 1-15, 2001. [46] H. Lai and T. C. Yang, “A System Architecture for Intelligent Browsing on the Web,” Decision Support Systems, Vol. 28, No. 3, pp. 219-239, 2000. [47] S. Lawrence and C. L. Giles, “Searching the World Wide Web,” Science, Vol. 280, No. 5360, pp. 98-100, 1998. [48] C. H. Lee, Y. H. Kim, and P. K. Rhee, “Web personalization expert with combining collaborative filtering and association rule mining technique,“ Expert Systems with Applications, Vol. 21, No. 3, October, pp. 131-137, 2001. [49] T. X. Lin, “HitRank: Search Ranking Based on Mining User-oriented Feedback,” Master Thesis, National Taiwan University of Science and Technology, 2002. [50] S. K. Madria, S. Bhowmick, W. Ng, and E. Lim, “Research Issues in Web Data Mining,” Proceedings of 1st International Conference on Data Warehousing and Knowledge Discovery, pp. 303-312, 1999. [51] U. Manber, A. Patel, and J. Robision, “Experience with Personalization on YAHOO!,” Communications of the ACM, Vol. 43, No. 8, pp. 35-39, 2000. [52] F. Menczer, “Complementing Search Engine with Online Web Mining Agents,” Decision Support Systems, Vol. 35, No. 2, pp. 195-212, 2003. [53] W. Meng, C. Yu, and K. Liu, “Building Efficient and Effective Metasearch Engines, ACM Computing Surveys,” Vol. 34, No. 1, pp. 48-89, March 2002. [54] B. Mobasher, H. Dai, T. Luo, and M. Nakagawa, “Effective Personalization Based on Association Rule Discovery from Web Usage Mining,” In Proceedings of the 3rd ACM Workshop on Web Information and Data Management, pp. 9-15, 2001. [55] B. Mobasher, R. Cooley, and J. Srivastava, “Automatic Personalization Based on Web Usage Mining,” Communications of the ACM, Vol. 43, No. 8, pp. 142-151, August 2000. [56] Z. Nick and P. Themis, “Web search using a genetic algorithm", IEEE Internet Computing, Vol. 5, No. 2, pp. 18-26, March-April 2001. [57] B.U. Oztekin, G. Karypis, and V. Kumar, “Expert Agreement and Content Based Reranking in a Meta Search Environment using Mearf,” Proceedings of the eleventh international conference on World Wide Web, pp. 333-344, 2002. [58] B. Padmanabhan, Z. Q. Zheng, and S. O. Kimbrough, “Personalization from Incomplete Data: What You Don’t Know Can Hurt,” The 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2001), pp. 154-163, August 2001. [59] J. Pitkow and P. Pirolli, “Mining Longest Repeating Subsequences to Predict World Wide Web Surfing,” In Proceedings of the Second USENIX Symposium on Internet Technologies and Systems, pp. 139-150, 1999. [60] G. Salton, A. Wong, and C. S. Yang, “A vector space model for automatic indexing,” Communications of the ACM, Vol. 18, No. 11, pp. 613-620, 1975. [61] G. Salton and M. J. McGill, Introduction to Modern Information Retrieval, McGraw-Hill, 1983. [62] E. Selberg and O. Etzioni, “The metacrawler architecture for resource aggregation on the web,” IEEE Expert, Vol. 12, No. 1, pp. 8-14, 1997. [63] Z. Shanfeng, D. Xiaotie, C. Kang, and Z. Weimin, “Using Online Relevance Feedback to Build Effective Personalized Metasearch Engine,” In Proceedings of the Second International Conference on Web Information Systems Engineering, Vol. 1, pp. 262-268, 3-6 December 2001. [64] C. Silverstein, M. Henzinger, H. Marais, and M. Moricz, “Analysis of a Very Large AltaVista Query Log,” SRC Technical Note 1998-014. [65] A. Spink , D. Wolfram, B. J. Jansen, and T. Saracevic, “Searching the web: The public and their queries,” Journal of the American Society for Information Science, Vol. 53, No. 2, pp. 226-234, 2001. [66] J. Srivastava, R. Cooley, M. Deshpande, and P-N Tan, “Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data,” ACM of SIGKDD Explorations, Vol. 1, No. 2, pp. 12-23, 2000. [67] R. E. Walope, R. H. Myers, and S. L. Myers, Probability and Statistics for Engineers and Scientists, sixth edition, Prentice Hall, New Jersey, 1998. [68] J. Wen, J. Nie, and H. Zhang, “Query Clustering Using User Logs,” ACM Transactions on Information Systems, Vol. 20, No. 1, pp. 59-81, 2002. [69] R. W. White, I. Ruthven, and J. M. Jose, “Finding Relevant Document using top Ranking Sentences: An Evaluation of Two Alternative Schemes”, In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 57-64, 2002. [70] R. W. White, I. Ruthven, and J. M. Jose, “The use of implicit evidence for relevance feedback in web retrieval”, In Proceedings of 24th BCS-IRSG European Colloquium on IR Research. Lecture notes in Computer Science 2291, Glasgow, pp. 93-109, 2002. [71] Y. H. Wu, Y. C. Chen, and A. L. P. Chen, “Enabling Personalized Recommendation on the Web Based on User Interests and Behaviors,” In Proceedings of 11th International Workshop on Research Issues in Data Engineering, pp. 17-24, 2001. [72] B. Yuwono and D. Lee, “Search and Ranking Algorithms for Locating Resources on the World Wide Web,” Proceeding of 12th International Conference on Data Engineering, New Orleans, pp. 164-171, Feb. 1999. [73] B. Yuwono and D. Lee, “Server ranking for distributed text resource systems on the Internet,” In proceeding of the 5th International Conference Systems For Advanced Application, Melbourne, pp. 391-400, Australia (April 1997). [74] D. Zhang and Y. Dong, “An Efficient Algorithm to Rank Web Resources,” In Proceedings of 9th International World Wide Web Conference, pp. 449-455, 2000. URL lists: [75] AltaVista, http://www.altavista.com. [76] Clever, http://www.almaden.ibm.com/cs/k53/clever.html. [77] Excite, http://www.excite.com. [78] Google, http://www.google.com. [79] Metacrawler, http://www.metacrawler.com. [80] NEC Research Institute ResearchIndex, http://citeseer.nj.nec.com. [81] OPENFIND, http://www.OPENFIND.com.tw. [82] ProFusion, http://www.profusion.com. [83] Savvy, http://www.search.com. [84] Search Engine Watch, http://www.searchenginewatch.com. [85] Teoma, http://www.teoma.com/. [86] WISEnut, http://www.wisenut.com. [87] Yahoo, http://www.yahoo.com. [88] YAM, http://www.yam.com.
|