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Course Web Pages - Fall 2012 - LIBR 202-13 Greensheet - Bibliography

LIBR 202
Information Retrieval
Bibliograhpy

Dr. Geoffrey Z. Liu
E-mail

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Part A: Books on Information Retrieval

• Allen, T.J. (1977). Managing the Flow of Technology: Technology Transfer and the Dissemination of Technological Information within the R and D Organization. Cambridge, Mass.: The MIT Press.
• Baeza-Yates, R., & Ribeiro-Neto, B. (1999). Modern Information Retrieval. 1st ed. Addison Wesley.
• Belew, R.K. (2000). Find Out About: A Cognitive Perspective on Search Engine Technology and the WWW. Cambridge, UK : Cambridge University Press.
• Date, C.J. (1995). An Introduction to Database Systems. 6th ed. Reading, MA: Addison-Wesley.
• Frants, V.I., Shapiro, J., & Voiskunskii, V.G. (1997). Automated Information Retrieval : Theory and Methods. San Diego, Calif. : Academic Press.
• Hagler, R. (1991). The Bibliographic Record and Information Technology. 2nd ed. Chicago: American Library Association.
• Korfhage, R. R. (1997). Information Storage and Retrieval. 1st ed. Wiley.
• Manning, C. D., Raghavan, P., & Schutze, H. (2008). Introduction to Information Retrieval. 1st ed. Cambridge University Press.
• Meadow, C.T., Boyce, B. R., & Kraft, D. H. (2007). Text Information Retrieval Systems. 3rd ed. Academic Press.
• Pao, M.L. (1989). Concepts of Information Retrieval. Englewood, CO : Libraries Unlimited.
• Rowley, J.E. (1987). Organizing Knowledge: An Introduction to Information Retrieval. Aldershot, England: Gower.
• Salton, G. (1989). Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer. Reading, MA : Addision-Wesley.
• Salton, G. & McGill, M.J. (1983). Introduction to Modern Information Retrieval. New York: McGraw-Hill.
• Schneiderman, B. (1992). Designing the User Interface: Strategies for Effective Human-Computer Interaction. 2nd ed. Reading, MA : Addison-Wesley.
• Soergel, D. (1985). Organizing Information: Principles of Database and Retrieval Systems. Orlando, FL: Academic Press.
• van Herwijnen, E. (1994). Practical SGML. 2nd ed. Boston: Kluwer Academic Publishers.
• Xie, I. (2008). Interactive Information Retrieval in Digital Environments. Hershey, PA : IGI Publishing.

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Part B: Journal Articles Listed by Topics

Information Science

• Bates, M. (1999). The invisible substrate of information science. Journal of the American Society for Information Science, 50, 1043-1050.
• Jarvelin, K. (2007). An analysis of two approaches in information retrieval: From frameworks to study designs. Journal of the American Society for Information Science and Technology, 58(7), 971-986.
• Kari, J. & Hartel, J. (2007). Information and higher things in life: Addressing the pleasurable and the profound in information science. Journal of the American Society for Information Science and Technology, 58(8), 1131-1147.

Problem Solving, Cognition, and Information Seeking Behavior

• Agosto, D.E. & Hughes-Hassell, S. (2005). People, places, and questions: An investigation of the everyday life information-seeking behaviors of urban young adults. Library & Information Science Research, 27, 141-163.
• Carley, K. & Palmquist, M. (1992). Extracting, representing, and analyzing mental models. Social Forces, 70(3), 601-636.
• Cothey, V. (2002). A longitudinal study of World Wide Web users' information-searching behavior. Journal of the American Society for Information Science and Technology, 53(2), 67-78.
• Dalrymple, P.W. (1990). Retrieval by reformulation in two library catalogs: Toward a cognitive model of searching behavior. Journal of the American Society for Information Science, 41(4), 272-281.
• Daniels, P.J. (1986). Progress in documentation: Cognitive models in information retrieval: An evaluative review. Journal of Documentation, 42(4), 272-304.
• Dennis, S., Bruza, P., & McArthur, R. (2002). Web searching: A process-oriented experimental study of three interactive search paradigms. Journal of the American Society for Information Science and Technology, 53(2), 120-133.
• Dervin, B., & Nilan, M. (1996). Information needs and uses. Annual Review of Information Science and Technology (ARIST), Chapter One. White Plains, NY: Knowledge Industry Publications, X. vol. 21, 3-33.
• Fine, S. (1994). Research and the psychology of information use. Library Trends, 441-460.
• Huston, M.M. (1991). Windows into the search process: An inquiry into dimensions of online information retrieval. Online Review, 227-243.
• Ingwersen, P. (1982). Search procedures in the library: Analyzed from the cognitive point of view. Journal of Documentation, 38(3), 165-191.
• Ingwersen, P. (1996). Cognitive perspectives of information retrieval interaction: Elements of a cognitive IR theory. Journal of Documentation, 52(1), 3-50.
• Jansen, B. J., Spink, A., & Koshman, S. (2007).Web searcher interaction with the Dogpile.com metasearch engine. Journal of the American Society for Information Science and Technology 589, 744-755.
• Kim, K.S. & Allen, B. (2002). Cognitive and task influences on web searching behavior. Journal of the American Society for Information Science and Technology, 53(2), 109-119.
• Kuhlthau, Carol C. and Tama, S. L.(2001).Information search processof lawyers: A call for "just for me" information services. Journal of Documentation, 57, 25-43.
• Liddy, E.D. & Jorgensen, C. (1993). Modeling information seeking behaviors in index use. Proceedings of the 56th ASIS Annual Meeting: Integrating Technologies, Converging Professions, v.30, pp.185-190. Medford, NJ: Learned Information.
• Linde, L.K. (1986). Artificial intelligence: What is domain skill? Frames in information seeking. Behavioral Science, 31, 89-102.
• Markey, K. (2007). Twenty-five years of end-user searching, Part I: Research findings. Journal of the American Society for Information Science and Technology, 58(8), 1071-1081.
• Markey, K. (2007). Twenty-five years of end-user searching, Part II: Future research directions. Journal of the American Society for Information Science and Technology, 58(8), 1123-1130.
• Najarian, S. E. (1981). Organizational factors in human memory: Implications for library organization and access systems. The Library Quarterly, 51(3), 269-291.
• Norman, D.A. (1986). Cognitive engineering. In Norman, D. A. & Draper, S.W. (Eds.)., User Centered System Design, pp.31-61. Hillsdale, New Jersey: Lawrence Erlbaum Associates.
• Rowley, J., & Urquhart, C. (2007). Understanding student information behavior in relation to eletronic information services: Lessons from longitudinal monitoring and evaluation, Part 1. Journal of the American Society for Information Science and Technology, 58(8), 1162-1174.
• Simon, H.A. (1981). Information-processing models of cognition. Journal of the American Society for Information Science, 32(5), 364-377.
• Soergel, D. (1985). Assessment of user's problems and needs. Organizing Information: Principles of Database and Retrieval Systems (pp.93-107). Orlando, FL: Academic Press.
• Urquhart, C. , & Rowley, J. (2007). Understanding student information behavior in relation to eletronic information services: Lessons from longitudinal monitoring and evaluation, Part 2. Journal of the American Society for Information Science and Technology, 58(8), 1188-1197.
• Xu, Y. (2007). The dynamics of interactive information retrieval behavior, Part I: An activity theory perspective. Journal of the American Society for Information Science and Technology, 58(7), 958-970.
• Xu, Y., & Liu, C. (2007). The dynamics of interactive information retrieval behavior, Part II: An empirical study from the activity theory perspective. Journal of the American Society for Information Science and Technology, 58(7), 987-998.

Subject Analysis and Classification

• Bates, M.J. (1986). Subject access in online catalogs: A design model. Journal of the American Society for Information Science, 37(6), 357-375.
• Beghtol, C. (1986). Semantic validity: Concepts of warrant in bibliographic classification systems. Library Resources & Technical Services, 109-125.
• Borgman, C.L. (1986). The user's mental model of an information retrieval system: An experiment on a prototype online catalog. International Journal of Man- Machine Studies, 24, 47-64.
• Gil-Leiva, I., & Alonso-Arroyo, A. (2007). Keywords given by authors of scientific articles in database descriptors. Journal of the American Society for Information Science and Technology, 58(8), 1175-1187.
• Haas, Stephanie W. & Travers, Debbie A. (2004). Issues in the development of a thesaurus for patients’ chief complaints in the emergency department. 67^th Proceedings of the ASIS&T annual meeting, 41, 411-417
• Jiang, S. (2007). Into the source and history of Chinese culture: Knowledge classification in ancient China. Libraries & the Cultural Record, 42(1).
• Kwasnik, B.H. (1992). The role of classification structures in reflecting and theory building. Proceedings of the 3rd ASIS SIG/CR Classification Research Workshop, pp.63-81.

Metadata & Information Representation

• Elings, Mary W. and Waibel, Gunter (2007). Metadata for all: Descriptive standards and metadata sharing across libraries, archives, and museums. FirstMonday 12. Retrieved August 1, 2007, from http://www.firstmonday.org/issues/issue12_3/elings/index.html
• Golder, S. A. & Huberman, B. A. (2005). The structure of collaborative tagging systems. arXiv.org. Retrieved August 6, 2007, from http://arxiv.org/ftp/cs/papers/0508/0508082.pdf [PDF]

User Interfaces & Information Visualization

• Bates, M. J. (1989). The design of browsing and berrypicking techniques for the online search interface. Online Review, 13(5), 407-424.
• Bayer, A. E., Smart, J. C., & McLaughlin, G. W. (1990). Mapping the intellectual structure of a scientific subfield through author cocitations. Journal of the American Society for Information Science, 41(6), 444-452 .
• Belkin, N. J., Marchetti, P. G., & Braque, C. (1993). Design of an interface to support user interaction in information retrieval. Information Processing and Management, 29(3), 325- 344.
• Buckland, M. K. & Florian, D. (1991). Expertise, task complexity, and artificial intelligence: A conceptual framework. Journal of the American Society for Information Science, 42(9), 635-643.
• Farooq, M. U. & Dominick, W. D. (1988). A survey of formal tools and models for developing user interfaces. International Journal of Man-Machine Studies, 29, 479-496.
• Large, A., Beheshti, J., & Rahman, T. (2002). Design criteria for children's web portals: The users speak out. Journal of the American Society for Information Science and Technology, 53(2), 79-94.
• Hearst, M. A. (1997). Interfaces for searching the Web. Scientific American, 276(3), 52-56.
• Johnson-Eilola, J. (2001). Little machines: Understanding users understanding interfaces. ACM Journal of Computer Documentation 25, 119-127.
• Large, A., Beheshti, J., Tabatabaei, N, & Nesset, V. (2009). Developing a visual taxonomy: Children's views on aesthetics. Journal of the American Society for Information Science and Technology, 60(9), 1808-1822.
• Lynch, C. (1997). Searching the Internet. Scientific American, 276(3), 68-72.
• Marchionini, G. (1992). Interfaces for end-user information seeking. Journal of the American Society for Information Science, 43(2), 156- 163.
• Marchionini, G., Dwiggins, S., Katz, A., & Lin, X. (1993). Information seeking in full-text end-user-oriented search systems: The roles of domain and search expertise. Library and Information Science Research, 15, 35-69.
• Mat-Hassan, M. & Levene, M.(2001). Can navigational assistance improve search experience? A user study. FirstMonday 6. Retrieved August 1, 2007, from http://www.firstmonday.org/issues/issue6_9/mat/index.html.
• McCain, K. W. (1990). Mapping authors in intellectual space: A technical overview. Journal of the American Society for Information Science, 41(6), 433-443.

Relevance Judgment and System Evaluation

• Applegate, R. (1993). Models of user satisfaction: Understanding false positives. RQ, 525-539.
• Bodoff, D. (2006). Relevance for browsing, relevance for searching.Journal of the American Society for Information Science and Technology, 57, 69-86
• Fitzgerald, M.A. & Galloway, C. (2001). Relevance judging, evaluation, and decision making in virtual libraries: A descriptive study. Journal of the American Society for Information Science and Technology, 52(12), 989-1010.
• Harter, S. P. (1992). Psychological relevance and information science. Journal of the American Society for Information Science, 43(9), 602-615. 
• Hildreth, C.  R. (2001). Accounting for users' inflated assessments of on-line catalogue search performance and usefulness: An experimental study. Information Research 6. Available at http://InformationR.net/ir/6-2/paper101.html
• Maron, M. E. & Blair, D. C. (1985). An evaluation of retrieval effectiveness for a full-text document-retrieval system. Communications of the ACM, 28 (3), 289-299.
• Mizzaro, S. (1997). Relevance: The whole history. Journal of the American Society for Information Science, 48(9), 810-832.
• Regazzi, J. J. (1988). Performance measures for information systems: An experimental approach. Journal of the America Society for Information Science, 39(4), 235-251.
• Robertson, S. E., M. E. Maron, M. E., & Cooper, W. S. (1982). Probability of relevance: A unification of two competing models for document retrieval. Information Technology: Research and Development, 1-21.
• Saracevic, T. (1975). Relevance: A review of and a framework for the thinking on the notion of information science. Journal of the American Society of Information Science, 26(6), 321-343.
• Saracevic, T. (1975). Relevance: A review of and a framework for the thinking on the notion of information science. Part II: Nature and manifestations of relevance. Journal of the American Society of Information Science, 58(1), 1915-1933.
• Saracevic, T. (1975). Relevance: A review of and a framework for the thinking on the notion of information science. Part III: Behavior and effects of relevance. Journal of the American Society of Information Science, 58, 2126-2144.
• Sutton, S. A. (1994). The role of attorney mental models of law in case relevance determinations: An exploratory analysis. Journal of the American Society of Information Science, 45(3), 186-200.
• Voorhees, E. M. (2005). Improving information access through evaluation. Bulletin of the American society for Information Science and Technology, 32, 16-21.
• Wacholder, N., Kellly, D., Kantor, P., Rittman, R., Sun, Y., Bai, B., Small, S., Yamrom, B., & Strzalkowski, T. (2007). A model for quantitative evaluation of an end-to-end question-answering system. Journal of the American Society for Information Science and Technology, 58(8), 1082-1099.
• Wang, P. & Soergel, D. (1993). Beyond topical relevance: Document selection behavior of real users of IR systems. Proceedings of the 56th ASIS Annual Meeting: Integrating Technologies; Converging Professions (pp.87-92). Medford, NJ: Learned Information.
• Zhang, X. (2002). Collaborative relevance judgment: A group consensus method for evaluating user search performance. Journal of the American Society for Information Science and Technology, 53(3), 220-231.

Relevance Feedback, User Profiling, and Automated Search Assistance

• Jansen, B.J. & McNeese, M.D. (2005). Evaluating the effectiveness of and patterns of interactions with automated searching assistance. Journal of the American Society for Information Science and Technology, 56(14), 1480-1503.

Full-Text IR Systems and Future Trends

• Cooper, W. S., & Maron, M. E. (1978). Foundations of probabilistic and utility- theoretic indexing. Journal of the Association for Computing Machinery, 25(1), 67-80.
• Frisse, M. F. & Cousins, S. B. (1992). Models for hypertext. Journal of the American Society for Information Science, 43(2), 183-191.
• Gauch, S. (1992). Intelligent information retrieval: An introduction. Journal of the American Society for Information Science, 43(2), 175-182.
• Harman, D. (1992). User-friendly systems instead of user-friendly front-ends. Journal of the American Society for Information Science, 43(2), 164-174.
• Latta, G. F. & Swigger, K. (1992). Validation of the repertory grid for use in modeling knowledge. Journal of the American Society for Information Science, 43(2), 115-129.
• Maron, M. E. (1988). Probabilistic design principles for conventional and full-text retrieval systems. Information Processing and Management, 24(3), 249-255.
• Maron, M. E. & Kuhns, J. L. (1960). On relevance, probabilistic indexing and information retrieval. Journal of the Association of Computing Machinery, 7(30), 216-243.
• Salminen, A. & Watters, C. (1992). A two-Level structure for textual databases to support hypertext access. Journal of the American Society for Information Science, 43(6), 432- 447.
• Smith, E. S. (1993). On the shoulder of giants: From Boole to Shannon to Taube: The origins and development of computerized information from the mid-19th century to the present. Information Technology and Libraries, 12(2), 217-226.

Web Indexing and Web Searching Algorithms

• Bar-Ilan, J. (2006). Web links and search engine ranking: The case of Google and the query "Jew". Journal of the American Society for Information Science and Technology, 57(12), 1581-1589.
• Naing, M., Lim, E., &Chiang, R. (2006). Extracting link chains of relationship instances from a web site. Journal of the American Society for Information Science and Technology, 57(12), 1590-1605.

Multimedia IR

• Shah, B., Raghavan, V., Dhatric, P., & Zhao, X. (2006). A cluster-based approach for efficient content-based image retrieval using a similarity-preserving space transformation method. Journal of the American Society for Information Science and Technology, 57(12), 1694-1706.

Digital Libraries

• Marchionini, G., Wildemuth, B.M., & Geisler, G. (2006). The open video digital library: A Mobius strip of research and practice. Journal of the American Society for Information Science and Technology, 57(12), 1629-1643.

Additional Resources on IR

• Special Interest Group on Information Retrieval. (1999- ). SIGIR Forum.

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