%Original file available from http://www.cs.odu.edu/~jbollen/bibliographies/bibtex/IR_met.bib %Last update: Friday 8 July 2005 %Current number of entries: 23 @ARTICLE{simili:hamers1989, title = {Similarity measures in scientometric research. The Jaccard index versus Saltons cosine formula}, year = 1989, author = { Lieve Hamers and Yves Hemeryck and Guido Herweyers and Marc Janssen and Hans Keters and Ronald Rousseau and Andrea Vanhoutte}, journal = {Information processing and management}, volume = 25, number = 3, pages = {315--333}, url = {http://linkseeker.lanl.gov/lanl?sid=SciSearchPlus&pid=/recs/sici03/0306-4573/25/3/315_HAMERS-SMSRJIVSSCF}, } @inproceedings{effect:mobasher2001, author = {Bamshad Mobasher and Honghua Dai and Tao Luo and Miki Nakagawa}, title = {Effective personalization based on association rule discovery from web usage data}, booktitle = {Proceeding of the third international workshop on Web information and data management}, year = {2001}, isbn = {1-58113-444-4}, pages = {9--15}, location = {Atlanta, Georgia, USA}, doi = {http://80-doi.acm.org.proxy.lib.odu.edu/10.1145/502932.502935}, publisher = {ACM Press}, } @inproceedings{collec:wasfi1999, author = {Ahmad M. {Ahmad Wasfi}}, title = {Collecting user access patterns for building user profiles and collaborative filtering}, booktitle = {Proceedings of the 4th international conference on Intelligent user interfaces}, year = {1999}, isbn = {1-58113-098-8}, pages = {57--64}, location = {Los Angeles, California, United States}, doi = {http://80-doi.acm.org.proxy.lib.odu.edu/10.1145/291080.291091}, publisher = {ACM Press}, } @inproceedings{imaged:shyu2003, author = {Mei-Ling Shyu and Shu-Ching Chen and Min Chen and Chengcui Zhang and Kanoksri Sarinnapakorn}, title = {Image database retrieval utilizing affinity relationships}, booktitle = {Proceedings of the first ACM international workshop on Multimedia databases}, year = {2003}, isbn = {1-58113-726-5}, pages = {78--85}, location = {New Orleans, LA, USA}, doi = {http://80-doi.acm.org.proxy.lib.odu.edu/10.1145/951676.951691}, publisher = {ACM Press}, } @inproceedings{effect:sun2003, author = {Jun Sun and Zhulong Wang and Hao Yu and Fumihito Nishino and Yukata Katsuyama and Satoshi Naoi}, title = {Effective text extraction and recognition for WWW images}, booktitle = {Proceedings of the 2003 ACM symposium on Document engineering}, year = {2003}, isbn = {1-58113-724-9}, pages = {115--117}, location = {Grenoble, France}, doi = {http://80-doi.acm.org.proxy.lib.odu.edu/10.1145/958220.958241}, publisher = {ACM Press}, } @article{applyi:fung1995, author = {Robert Fung and Brendan {Del Favero}}, title = {Applying Bayesian networks to information retrieval}, journal = {Commun. ACM}, volume = {38}, number = {3}, year = {1995}, issn = {0001-0782}, pages = {42--ff.}, doi = {http://doi.acm.org/10.1145/203330.203340}, publisher = {ACM Press}, } @INCOLLECTION{buildi:sanderson2000, author = {Mark Sanderson and Dawn Lawrie}, title = {Building, testing, and applying concept hierarchies}, year = {2000}, booktitle = {Advances in Information Retrieval}, editor = {W. Bruce Croft}, publisher = {Kluwer Academic Publishers}, address = {Dordrecht}, pages = {235--266}, } @ARTICLE{approx:hall1980, author = {Patrick A. V. Hall and Geoff R. Dowling}, title = {Approximate String Matching}, journal = {ACM Computing Surveys}, year = 1980, volume = 12, number = 4, month = {December}, pages = {381--402}, abstract = {Overview of different approximate string matching techniques, including the Levensthein distance. Use this as a general reference to provide more information in Levenshtein string distance measure} } @ARTICLE{algori:porter1980, author = {M. Porter}, title = {An algorithm for suffic stripping}, year = 1980, journal = {Automated Library and Information Systems}, volume = {14}, number = {3}, pages = {130--137}, } @ARTICLE{usingl:berry1995, author = {M.W. Berry and S.T. Dumais and G.W. O'Brien}, year = 1995, title = {Using linear algebra for intelligent information retrieval}, journal = {SIAM review}, volume = 37, number = 4, pages = {573--595} } @INPROCEEDINGS{usingl:foltz1990, author = {Peter W. Foltz}, editor = {R. B. Allen}, year = 1990, title = {Using Latent Semantic Indexing for Information Filtering}, booktitle = {Proceedings of the {C}onference on {O}ffice {I}nformation {S}ystems}, pages = {40--47}, address = {Cambridge, MA} } @INPROCEEDINGS{role:smith1993, title = {The role of built-in knowledge in adaptive interface systems}, author = {Daniel Crow and Barbara Smith}, year = 1993, booktitle = {{P}roceedings of the 1st international conference on Intelligent user interfaces}, pages = {97--104}, address = {Orlando, {FL}}, month = {January}, } @article{experi:shapira1999, author = {B. Shapira and P. Shoval and U. Hanani}, title = {Experimentation with an information filtering system that combines cognitive and sociological filtering integrated with user stereotypes}, journal = {Decision Support Systems}, volume = {27}, number = {1-2}, pages = {5--24}, month = {November}, year = {1999}, abstract = {A dual-method model and system for filtering and ranking relevance of information is presented. One method is cognitive filtering, while the other is sociological filtering, which is integrated with user stereotypes. A prototype system was developed to test the applicability of the model for filtering e-mail messages, and experiments were run to determine the effects of combining the two methods in various filtering strategies. Results reveal that although cognitive filtering alone is usually more effective than sociological filtering alone, the combination of both methods yield better results than using each method individually. Ordinarily, the best filtering strategies are achieved when the two methods are used in parallel, or when cognitive filtering is the primary method, followed by sociological filtering. We conclude that the optimal filtering strategy of combining cognitive and sociological filtering is stereotype dependent; i.e., for each user stereotype, there may be a specific combination of the cognitive and sociological filtering that yields best results. (C) 1999 Elsevier Science B.V. All rights reserved.}, keywords = {INFORMATION FILTERING ; INFORMATION RETRIEVAL ; FILTERING SYSTEMS ; COGNITIVE FILTERING ; SOCIOLOGICAL FILTERING ; COLLABORATIVE FILTERING ; CONTENT-BASED FILTERING ; CLUSTER ANALYSIS ; USER STEREOTYPES ; USER PROFILES ; EXPERIMENTATION ; RELEVANCE RANKING ; INDEXING}, } @BOOK{inform:grossman1998, author ={Davi A. Grossmman and Ophir Frieder}, title = {Information Retrieval. {A}lgorithms and {H}euristics}, year = 1998, publisher = {Kluwer Academic Publishers}, address = {Boston}, } @ARTICLE{larges:letsche1995, author = {Todd A. Letsche and Michael W. Berry}, year = 1997, title = {Large-Scale Information Retrieval with Latent Semantic Indexing}, journal = {Information Sciences}, volume = 100, pages = {105-137} } @BOOK{fuzzys:miyamoto1990, author = {S. Miyamoto}, title = {Fuzzy sets in Information Retrieval and Cluster Analysis}, year = 1990, publisher = {Kluwer Academic Publishers}, } @BOOK{textre:losee1998, author = {R. M. Losee}, title = {Text Retrieval and Filtering: Analytic Models of Performance}, year = 1998, publisher = {Kluwer Academic Publishers}, } @INCOLLECTION{repres:nakamura1982, author = {K. Nakamura and S. Iwai}, title = {A representation of analogical inference by fuzzy sets and its application to information retrieval systems}, year = {1982}, booktitle = {Fuzzy Information and Decision Processes}, editor = {Gupta and Sanchez}, publisher = {North-Holland}, pages = {373--386}, } @ARTICLE{contri:croft1987, author = {W. B. Croft}, title = {Contributions from IR research}, year = 1987, journal = {Information Processing and Management}, volume = 23, number = 4, pages = {249--254}, } @INPROCEEDINGS{applic:kleinberg1999, title = {Applications of Linear Algebra in Information Retrieval and Hypertext Analysis}, author = {Jon M. Kleinberg and Andrew Tomkins}, year = 1999, pages = {185--193}, booktitle = {Proceedings of the 18th {ACM} {SIGACT}-{SIGMOD}-{SIGART} {S}ymposium on {P}rinciples of {D}atabase {S}ystems}, publisher = {{ACM} Press}, address = {Philadelphia, {PA}}, year = {1999}, month = {May}, isbn = {1-58113-062-7}, } @ARTICLE{inform:mock1997, author = {Kenrick J. Mock and V. Rao Vemuri}, year = 1997, title = {Information Filtering Via Hill-Climbing, Wordnet, and Index Patterns}, journal = {Information Processing and Management}, volume = 33, number = 5, pages = {633-644}, abstract = {The recent growth of the Internet has left many users awash in a sea of information. This development has spawned the need for intelligent filtering systems. This paper describes work implemented in the INFOS (Intelligent News Filtering Organizational System) project that is designed to to reduce the user's search burden by automatically categorizing data as relevant or irrelevant based upon user interests. These predictions are learned automatically based upon features taken from input articles and collaborative features derived from other users. The filtering is performed by a hybrid technique that combines elements of a keyword-based hill climbing method, knowledge-based conceptual representation via WordNet, and partial parsing via index patterns. The Hybrid system integrating all these approaches benefits of each while maintaining robustness and scalability.} } @INPROCEEDINGS{making:frei1992, author = {H.P. Frei and D. Stieger}, month = {November}, year = 1992, title = {Making use of Hypertext Links when Retrieving Information}, booktitle = {Conference on {H}ypertext and {H}ypermedia, {P}roceedings of the {ACM} {C}onference on {H}ypertext}, address = {Milano, Italy}, pages = {102--111} } @INPROCEEDINGS{selforga:lin1991, author = {Xia Lin and Dagobert Soergel and Gary Marchionini}, title = {A self-organizing semantic map for information retrieval}, year = 1991, booktitle = {Procedings of ACM/SIGIR91: Conference on Research and Development in Information Retrieval}, publisher = {ACM}, address = {Chicago, USA}, month = {October}, pages = {262--269}, abstract = {A neural network's unsupervised learning algorithm, Kohonen's feature map, is applied to constructing a self-organizing semantic map for information retrieval. The semantic map visualizes semantic relationships between input documents, and has properties of economic representation of data with their interrelationships. The potentials of the semantic map include using a map as a retrieval interface for an on-line bibliographic system. A prototype system that demonstrates this potential is described. }, } @BOOK{textre:losee1998, author = {Robert M. Losee}, title = {Text Retrieval and Filtering. Analytic Models of Performance}, year = {1998}, publisher = {Kluwer Academic Publishers}, address = {Boston}, }