Abstract
Scientific digital library systems are a very promising application area for value-added expert advice services. Such systems could significantly reduce the search and evaluation costs of information products for students and scientists. This holds for pure digital libraries as well as for traditional scientific libraries with online public access catalogs (OPAC). In this contribution we first outline different types of recommendation services for scientific libraries and their general integration strategies. Then we focus on a recommender system based on log file analysis that is fully operational within the legacy library system of the Universität Karlsruhe (TH) since June 2002. Its underlying mathematical model, the implementation within the OPAC, as well as the first user evaluation is presented.
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Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Buneman, P., Jajodia, S. (eds.) Proc. ACM SIGMOD Int. Conf. on Management of Data, Washington, D.C., USA, vol. 22, ACM Press, New York (1993)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proc. 20th Very Large Databases Conf., Santiago, Chile, September 1994, pp. 487–499 (1994)
Ansari, A., Essegaier, S., Kohli, R.: Internet recommendation systems. Journal of Marketing Research 37, 363–375 (2000)
Apps, A., MacIntyre, R.: Prototyping Digital Library Technologies in zetoc. In: Agosti, M., Thanos, C. (eds.) ECDL 2002. LNCS, vol. 2458, pp. 309–323. Springer, Heidelberg (2002)
Aumann, Y., Lindell, Y.: A statistical theory for quantitative association rules. In: Chaudhuri, U., Madigan, D. (eds.) Proc. 5th ACM SIGKDD int. conference on Knowledge Discovey and Data Mining, San Diego, California, pp. 261–270. ACM press, New York (1999)
Balabanovic, M.: An adaptive web page recommendation service. In: Proc. 1st Int. Conf. on Autonomous Agents, Marina del Rey, California (February 1997)
Balabanovic, M., Shoham, Y.: Fab: Content-based, collaborative recommendation. Communications of the ACM 40(3), 66–72 (1997)
Bollen, J., Rocha, L.M.: Anadaptive systems approach to the implementation and evaluation of digital library recommendation systems. In: Borbinha, J.L., Baker, T. (eds.) ECDL 2000. LNCS, vol. 1923, pp. 356–359. Springer, Heidelberg (2000)
Brin, S., Motwani, R., Silverstein, C.: Beyond market baskets: Generalizing association rules to correlations. In: Peckman, J.M. (ed.) Proc. ACM SIGMOD Int. Conf. on Management of Data, Tucson, Arizona, vol. 26, pp. 265–276. ACM Press, New York (1997)
Die Deutsche Bibliothek. MAB: Maschinelles Austauschformat für bibliotheken, http://www.ddb.de/professionell/mab.htm
Ehrenberg, A.S.C.: Repeat-Buying: Facts, Theory and Applications, 2nd edn. Charles Griffin & Company Ltd., London (1988)
Fuhr, N., et al.: Daffodil: an integrated desktop for supporting high-level search activities in federated digital libraries. In: Agosti, M., Thanos, C. (eds.) ECDL 2002. LNCS, vol. 2458, pp. 597–612. Springer, Heidelberg (2002)
Geyer-Schulz, A., Hahsler, M., Jahn, M.: A customer purchase incidence model applied to recommender services. In: Kohavi, R., Masand, B., Spiliopoulou, M., Srivastava, J. (eds.) WebKDD 2001. LNCS (LNAI), vol. 2356, pp. 25–47. Springer, Heidelberg (2002)
Geyer-Schulz, A., Hahsler, M., Jahn, M.: Recommendations for virtual universities from observed user behavior. In: Gaul, W., Ritter, G. (eds.) Classification, Automation, and New Media, Studies in Classification, Data Analysis, and Knowledge Organization, vol. 20, pp. 273–280. Springer, Heidelberg (2002)
Geyer-Schulz, A., Hahsler, M., Neumann, A., Thede, A.: Behavior-based recommender systems as value-added services for scientific libraries. In: Bozdogan, H. (ed.) Statistical Data Mining and Knowledge Discovery. Chapmann & Hall, Sydney (2003)
Geyer-Schulz, A., Hahsler, M., Neumann, A., Thede, A.: An integration strategy for distributed recommender services in legacy library systems. In: Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Heidelberg (2003) (to appear)
Geyer-Schulz, A., Hahsler, M., Thede, A.: Comparing association-rules and repeat-buying based recommender systems in a B2B environment. In: Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Heidelberg (2003) (to appear)
The Stanford Digital Libraries Group. The stanford digital library project. Communications of the ACM 38(4), 59–60 (1995)
Hicks, D., Tochtermann, K., Kussmaul, A.: Augmenting digital catalogue functionality with support for customization. In: Proc. 3rd Int. Conf. on Asian Digital Libraries (2000)
Johnson, N.L., Kemp, A.W., Kotz, S.: Univariate Discrete Distributions. Wiley Series in Probability and Mathematical Statistics, 2nd edn. J. Wiley, Chichester (1993)
Klatt, R., et al.: Nutzung und Potenziale der innovativen Mediennutzung im Lernalltag der Hochschulen. BMBF-Studie (2001), http://www.stefi.de
Konstan, J., et al.: Grouplens: Applying Collaborative Filtering to Usernet News. Communications of the ACM 40(3), 77–87 (1997)
Mild, A., Natter, M.: Collaborative filtering or regression models for internet recommendation systems? Journal of Targeting, Measurement and Analysis for Marketing 10(4), 304–313 (2002)
Milgrom, P., Roberts, J.: Economics, Organization and Management, 1st edn. Prentice-Hall, Upper Saddle River (1992)
Papadakis, I., Andreou, I., Chrissikopoulos, V.: Interactive search results. In: Agosti, M., Thanos, C. (eds.) ECDL 2002. LNCS, vol. 2458, pp. 448–462. Springer, Heidelberg (2002)
Rolski, T., et al.: Stochastic processes for insurance and finance. J. Wiley, Chichester (1999)
Salton, G., Buckley, C.: Improving retrieval performance by relevance feedback. Journal of the American Society for Information Science 41(4), 288–297 (1990)
Virtual university of the Wirtschaftsuniversität Wien, http://vu.wu-wien.ac.at/
Wagner, U., Taudes, A.: Stochastic models of consumer behaviour. European Journal of Operational Research 29(1), 1–23 (1987)
Wilensky, R., et al.: Reinventing scholarly information dissemination and use. Technical report, University of California, Berkeley (1999), http://elib.cs.berkeley.edu/pl/about.html
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Geyer-Schulz, A., Neumann, A., Thede, A. (2003). Others Also Use: A Robust Recommender System for Scientific Libraries. In: Koch, T., Sølvberg, I.T. (eds) Research and Advanced Technology for Digital Libraries. ECDL 2003. Lecture Notes in Computer Science, vol 2769. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45175-4_12
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DOI: https://doi.org/10.1007/978-3-540-45175-4_12
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