Michael Hahsler

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Research on Recommender Systems

IT IS OFTEN NECESSARY TO MAKE CHOICES WITHOUT SUFFICIENT personal experience of the alternatives. In everyday life, we rely on recommendations from other people either by word of mouth, recommendation letters, movie and book reviews printed in newspapers, or general surveys such as Zagat's restaurant guides.
Recommender systems assist and augment this natural social process. In a typical recommender system people provide recommendations as inputs, which the system then aggregates and directs to appropriate recipients. In some cases the primary transformation is in the aggregation; in others the system's value lies in its ability to make good matches between the recommenders and those seeking recommendations.

From Recommender systems by Paul Resnick and Hal R. Varian (CACM, Volume 40 , Issue 3, 1997)

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[1] Michael Hahsler. Optimizing web sites for customer retention. In Bing Liu, Myra Spiliopoulou, Jaideep Srivastava, and Alex Tuzhilin, editors, Proceedings of the 2005 International Workshop on Customer Relationship Management: Data Mining Meets Marketing, November 18--19, 2005, New York City, USA, 2005. [ bib | .pdf ]
With customer relationship management (CRM) companies move away from a mainly product-centered view to a customer-centered view. Resulting from this change, the effective management of how to keep contact with customers throughout different channels is one of the key success factors in today's business world. Company Web sites have evolved in many industries into an extremely important channel through which customers can be attracted and retained. To analyze and optimize this channel, accurate models of how customers browse through the Web site and what information within the site they repeatedly view are crucial. Typically, data mining techniques are used for this purpose. However, there already exist numerous models developed in marketing research for traditional channels which could also prove valuable to understanding this new channel. In this paper we propose the application of an extension of the Logarithmic Series Distribution (LSD) model repeat-usage of Web-based information and thus to analyze and optimize a Web Site's capability to support one goal of CRM, to retain customers. As an example, we use the university's blended learning web portal with over a thousand learning resources to demonstrate how the model can be used to evaluate and improve the Web site's effectiveness.

[2] Andreas Geyer-Schulz, Michael Hahsler, Andreas Neumann, and Anke Thede. Behavior-based recommender systems as value-added services for scientific libraries. In Hamparsum Bozdogan, editor, Statistical Data Mining & Knowledge Discovery, pages 433--454. Chapman & Hall / CRC, July 2003. [ bib | .pdf ]
Amazon.com paved the way for several large-scale, behavior-based recommendation services as an important value-added expert advice service for online book shops. In this contribution we discuss the effects (and possible reductions of transaction costs) for such services and investigate how such value-added services can be implemented in context of scientific libraries. For this purpose we present a new, recently developed recommender system based on a stochastic purchase incidence model, present the underlying stochastic model from repeat-buying theory and analyze whether the underlying assumptions on consumer behavior holds for users of scientific libraries, too. We analyzed the logfiles with approximately 85 million HTTP-transactions of the web-based online public access catalog (OPAC) of the library of the Universität Karlsruhe (TH) since January 2001 and performed some diagnostic checks. The recommender service is fully operational within the library system of the Universität Karlsruhe (TH) since 2002/06/22.

[3] Andreas Geyer-Schulz, Michael Hahsler, Andreas Neumann, and Anke Thede. An integration strategy for distributed recommender services in legacy library systems. In M. Schader, W. Gaul, and M. Vichi, editors, Between Data Science and Applied Data Analysis, Proceedings of the 26th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Mannheim, July 22--24, 2002, Studies in Classification, Data Analysis, and Knowledge Organization, pages 412--420. Springer-Verlag, July 2003. [ bib | at the publisher ]
Scientific library systems are a very promising application area for recommender services. Scientific libraries could easily develop customer-oriented service portals in the style of amazon.com. Students, university teachers and researchers can reduce their transaction cost (i.e. search and evaluation cost of information products). For librarians, the advantage is an improvement of the customer support by recommendations and the additional support in marketing research, product evaluation, and book selection. In this contribution we present a strategy for integrating a behavior-based distributed recommender service in legacy library systems with minimal changes in the legacy system.

[4] Andreas Geyer-Schulz, Michael Hahsler, and Anke Thede. Comparing association-rules and repeat-buying based recommender systems in a B2B environment. In M. Schader, W. Gaul, and M. Vichi, editors, Between Data Science and Applied Data Analysis, Proceedings of the 26th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Mannheim, July 22--24, 2002, Studies in Classification, Data Analysis, and Knowledge Organization, pages 421--429. Springer-Verlag, July 2003. [ bib | at the publisher ]
In this contribution we present a systematic evaluation and comparison of recommender systems based on simple association rules and on repeat-buying theory. Both recommender services are based on the customer purchase histories of a medium-sized B2B-merchant for computer accessories. With the help of product managers an evaluation set for recommendations was generated. With regard to this evaluation set, recommendations produced by both methods are evaluated and several error measures are computed. This provides an empirical test whether frequent item sets or outliers of a stochastic purchase incidence model are suitable concepts for automatically generation recommendations. Furthermore, the loss function (performance measures) of the two models are compared and the sensitivity with regard to a misspecification of the model parameters is discussed.

[5] Andreas Geyer-Schulz and Michael Hahsler. Comparing two recommender algorithms with the help of recommendations by peers. In O.R. Zaiane, J. Srivastava, M. Spiliopoulou, and B. Masand, editors, WEBKDD 2002 - Mining Web Data for Discovering Usage Patterns and Profiles 4th International Workshop, Edmonton, Canada, July 2002, Revised Papers, Lecture Notes in Computer Science LNAI 2703, pages 137--158. Springer-Verlag, 2003. (Revised version of the WEBKDD 2002 paper “Evaluation of Recommender Algorithms for an Internet Information Broker based on Simple Association Rules and on the Repeat-Buying Theory”). [ bib | at the publisher | .pdf ]
Since more and more Web sites, especially sites of retailers, offer automatic recommendation services using Web usage mining, evaluation of recommender algorithms has become increasingly important. In this paper we present a framework for the evaluation of different aspects of recommender systems based on the process of discovering knowledge in databases introduced by Fayyad et al. and we summarize research already done in this area. One aspect identified in the presented evaluation framework is widely neglected when dealing with recommender algorithms. This aspect is to evaluate how useful patterns extracted by recommender algorithms are to support the social process of recommending products to others, a process normally driven by recommendations by peers or experts. To fill this gap for recommender algorithms based on frequent itemsets extracted from usage data we evaluate the usefulness of two algorithms. The first recommender algorithm uses association rules, and the other algorithm is based on the repeat-buying theory known from marketing research. We use 6 months of usage data from an educational Internet information broker and compare useful recommendations identified by users from the target group of the broker (peers) with the recommendations produced by the algorithms. The results of the evaluation presented in this paper suggest that frequent itemsets from usage histories match the concept of useful recommendations expressed by peers with satisfactory accuracy (higher than 70%) and precision (between 60% and 90%). Also the evaluation suggests that both algorithms studied in the paper perform similar on real-world data if they are tuned properly.

[6] Andreas Geyer-Schulz, Michael Hahsler, Andreas Neumann, and Anke Thede. Recommenderdienste für wissenschaftliche Bibliotheken und Bibliotheksverbünde. In Andreas Geyer-Schulz and Alfred Taudes, editors, Informationswirtschaft: Ein Sektor mit Zukunft, Symposium 4.--5. September 2003, Wien, Österreich, Lecture Notes in Informatics (LNI) P-33, pages 43--58. Gesellschaft für Informatik, 2003. [ bib | at the publisher ]
Wissenschaftliche Bibliotheken stellen ein vielversprechendes Anwendungsfeld für Recommenderdienste dar. Wissenschaftliche Bibliotheken können leicht kundenzentrierte Serviceportale im Stil von amazon.com entwickeln. Studenten, Universitätslehrer und -forscher können ihren Anteil an den Transaktionskosten (z.B. Such- und Bewertungskosten für Informationsprodukte) reduzieren. Für Bibliothekare liegt der Vorteil in einer Verbesserung der Kundenberatung durch Empfehlungen und einer zusätzlichen Unterstützung bei der Marktforschung, Produktbewertung und dem Bestandsmanagement. In diesem Beitrag präsentieren wir eine Strategie, mit der verhaltensbasierte, verteilte Recommenderdienste in bestehende Bibliothekssysteme mit minimalem Aufwand integriert werden können und berichten über unsere Erfahrungen bei der Einführung eines solchen Dienstes an der Universitätsbibliothek der Universität Karlsruhe (TH).

[7] Andreas Geyer-Schulz and Michael Hahsler. Evaluation of recommender algorithms for an internet information broker based on simple association rules and on the repeat-buying theory. In Brij Masand, Myra Spiliopoulou, Jaideep Srivastava, and Osmar R. Zaiane, editors, Fourth WEBKDD Workshop: Web Mining for Usage Patterns & User Profiles, pages 100--114, Edmonton, Canada, July 2002. [ bib | .pdf ]
Association rules are a widely used technique to generate recommendations in commercial and research recommender systems. Since more and more Web sites, especially of retailers, offer automatic recommender services using Web usage mining, evaluation of recommender algorithms becomes increasingly important. In this paper we first present a framework for the evaluation of different aspects of recommender systems based on the process of discovering knowledge in databases of Fayyad et al. and then we focus on the comparison of the performance of two recommender algorithms based on frequent itemsets. The first recommender algorithm uses association rules, and the other recommender algorithm is based on the repeat-buying theory known from marketing research. For the evaluation we concentrated on how well the patterns extracted from usage data match the concept of useful recommendations of users. We use 6 month of usage data from an educational Internet information broker and compare useful recommendations identified by users from the target group of the broker with the results of the recommender algorithms. The results of the evaluation presented in this paper suggest that frequent itemsets from purchase histories match the concept of useful recommendations expressed by users with satisfactory accuracy (higher than 70%) and precision (between 60% and 90%). Also the evaluation suggests that both algorithms studied in the paper perform similar on real-world data if they are tuned properly.

[8] Andreas Geyer-Schulz, Michael Hahsler, and Maximillian Jahn. A customer purchase incidence model applied to recommender systems. In R. Kohavi, B.M. Masand, M. Spiliopoulou, and J. Srivastava, editors, WEBKDD 2001 - Mining Log Data Across All Customer Touch Points, Third International Workshop, San Francisco, CA, USA, August 26, 2001, Revised Papers, Lecture Notes in Computer Science LNAI 2356, pages 25--47. Springer-Verlag, July 2002. (Revised version of the WEBKDD 2001 paper “A Customer Purchase Incidence Model Applied to Recommender Systems”). [ bib | at the publisher | .pdf ]
In this contribution we transfer a customer purchase incidence model for consumer products which is based on Ehrenberg s repeat-buying theory to Web-based information products. Ehrenberg s repeat-buying theory successfully describes regularities on a large number of consumer product markets. We show that these regularities exist in electronic markets for information goods, too, and that purchase incidence models provide a well founded theoretical base for re-commender and alert services. The article consists of two parts. In the first part Ehrenberg s repeat-buying theory and its assumptions are reviewed and adapted for web-based information markets. Second, we present the empirical validation of the model based on data collected from the information market of the Virtual University of the Vienna University of Economics and Business Administration from September 1999 to May 2001.

[9] Walter Böhm, Andreas Geyer-Schulz, Michael Hahsler, and Maximillian Jahn. Repeat buying theory and its application for recommender services. In O. Opitz and M. Schwaiger, editors, Exploratory Data Analysis in Empirical Research, Proceedings of the 25th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Munich, March 14--16, 2001, pages 229--239. Springer-Verlag, 2002. [ bib | at the publisher | .pdf ]
In the context of a virtual university's information broker we study the consumption patterns for information goods and we investigate if Ehrenberg's repeat-buying theory which successfully models regularities in a large number of consumer product markets can be applied in electronic markets for information goods too. First results indicate that Ehrenberg's repeat-buying theory succeeds in describing the consumption patterns of bundles of complementary information goods reasonably well and that this can be exploited for automatically generating anonymous recommendation services based on such information bundles. An experimental anonymous recommender service has been implemented and is currently evaluated in the Virtual University of the Vienna University of Economics and Business Administration at http://vu.wu-wien.ac.at.

[10] Wolfgang Gaul, Andreas Geyer-Schulz, Michael Hahsler, and Lars Schmidt-Thieme. eMarketing mittels Recommendersystemen. Marketing ZFP, 24:47--55, 2002. [ bib | at the publisher ]
Recommendersysteme liefern einen wichtigen Beitrag für die Ausgestaltung von eMarketing Aktivitäten. Ausgehend von einer Diskussion von Input/Output Charakteristika zur Beschreibung solcher Systeme, die bereits eine geeignete Unterscheidung praxisrelevanter Erscheinungsformen erlauben, wird motiviert, warum eine solche Charakterisierung durch die Einbeziehung methodischer Aspekte aus der Marketing Forschung angereichert werden muss. Ein auf der Theorie des Wiederkaufverhaltens basierendes Recommendersystem sowie ein System, das Empfehlungen mittels Analyse des Navigationsverhaltens von Site Besuchern erzeugt, werden vorgestellt. Am Beispiel der Amazon Site werden die Marketing Möglichkeiten von Recommendersystemen verdeutlicht. Abschließend wird zur Abrundung auf weitere Literatur mit Recommendersystem Bezug eingegangen. In einem Ausblick werden Hinweise gegeben, in welche Richtungen Weiterentwicklungen geplant sind.

[11] Andreas Geyer-Schulz, Michael Hahsler, and Maximillian Jahn. Recommendations for virtual universities from observed user behavior. In W. Gaul and G. Ritter, editors, Classification, Automation, and New Media, Proceedings of the 24th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Passau, March 15--17, 2000, pages 273--280. Springer-Verlag, 2002. [ bib | at the publisher | .pdf ]
Recently recommender systems started to gain ground in commercial Web-applications. For example, the online-bookseller amazon.com recommends his customers books similar to the ones they bought using the analysis of observed purchase behavior of consumers. In this article we describe a generic architecture for recommender services for information markets which has been implemented in the setting of the Virtual University of the Vienna University of Economics and Business Administration (http://vu.wu-wien.ac.at). The architecture of a recommender service is defined as an agency of interacting software agents. It consists of three layers, namely the meta-data management system, the broker management system and the business-to-customer interface.

[12] Andreas Geyer-Schulz, Michael Hahsler, and Maximillian Jahn. Wissenschaftliche Recommendersysteme in Virtuellen Universitäten. In H.-J. Appelrath, R. Beyer, U. Marquardt, H.C. Mayr, and C. Steinberger, editors, Unternehmen Hochschule, Wien, Österreich, September 2001. Symposium UH2001, GI Lecture Notes in Informatics (LNI). [ bib | at the publisher | .pdf ]
In diesem Beitrag wird die Rolle von Recommendersystemen und ihr Potential in der Lehr-, Lern- und Forschungsumgebung einer Virtuellen Universität untersucht.Die Hauptidee dieses Beitrags besteht darin, die Informationsaggregationsfähigkeiten von Recommendersystemen in einer Virtuellen Universität auszunutzen, um Tutoren-und Beratungsdienste in einer Virtuellen Universität automatisch zu verbessern, um damit Betreuung und Beratung von Studierenden zu personalisieren und für eine größere Anzahl von Teilnehmern bei gleichzeitiger Entlastung der Lehrenden verfügbar zu machen. Im zweiten Teil dieses Beitrags werden die Recommenderdienste von myVU, der Sammlung der personalisierten Dienste der Virtuellen Universität (VU) der Wirtschaftsuniversität Wien und ihre nicht-personalisierten Variantenbeschrieben, die im Wesentlichen auf beobachtetem Benutzerverhalten und, in der personalisierten Variante, zusätzlich auf Selbstselektion durch Selbsteinschätzung der Erfahrung in einem Fachgebiet beruhen. Abschließend wird noch der innovative Einsatz solcher Systeme diskutiert und an einigen Szenarien beschrieben.

[13] Andreas Geyer-Schulz, Michael Hahsler, and Maximillian Jahn. A customer purchase incidence model applied to recommender systems. In WEBKDD2001 Workshop: Mining Log Data Across All Customer TouchPoints, pages 35--45, San Francisco, CA, August 2001. [ bib | .pdf ]
In this contribution we transfer a customer purchase incidence model for consumer products which is based on Ehrenberg's repeat-buying theory to Web-based information products. Ehrenberg's repeat-buying theory successfully describes regularities in a large number of consumer product markets. We show that these regularities exist in electronic markets for information goods too, and that purchase incidence models provide a well founded theoretical foundation for recommender and alert systems. The article consists of three parts. First, we present the architecture of an information market and its instrumentation for collecting data on customer behavior. In the second part Ehrenberg's repeat-buying theory and its assumptions are reviewed and adapted for Web-based information markets. Finally, we present the empirical validation of the model based on data collected from the information market of the Virtual University of the Vienna University of Economics and Business Administration at http://vu.wu-wien.ac.at

[14] Andreas Geyer-Schulz, Michael Hahsler, and Maximillian Jahn. Educational and scientific recommender systems: Designing the information channels of the virtual university. International Journal of Engineering Education, 17(2):153--163, 2001. [ bib | .pdf ]
In this article we investigate the role of recommender systems and their potential in the educational and scientific environment of a Virtual University. The key idea is to use the information aggregation capabilities of a recommender system to improve the tutoring and consulting services of a Virtual University in an automated way and thus scale tutoring and consulting in a personalized way to a mass audience. We describe the recommender services of myVU, the collection of the personalized services of the Virtual University (VU) of the Vienna University of Economics and Business Administration which are based on observed user behavior and self assignment of experience which are currently field-tested. We show, how the usual mechanism design problems inherent to recommender systems are addressed in this prototype.

[15] Andreas Geyer-Schulz and Michael Hahsler. Automatic labelling of references for information systems. In Reinhold Decker and Wolfgang Gaul, editors, Classification and Information Processing at the Turn of the Millennium, Proceedings of the 23rd Annual Conference of the Gesellschaft für Klassifikation e.V., University of Bielefeld, March 10--12, 1999, Studies in Classification, Data Analysis, and Knowledge Organization, pages 451--459. Springer-Verlag, 2000. [ bib | at the publisher | .pdf ]
Today users of Internet information services like e.g. Yahoo! or AltaVista often experience high search costs. One important reason for this is the necessity to browse long reference lists manually, because of the well-known problems of relevance ranking. A possible remedy is to complement the references with automatically generated labels which provide valuable information about the referenced information source. Presenting suitably labelled lists of references to users aims at improving the clarity and thus comprehensibility of the information offered and at reducing the search cost. In the following we survey several dimensions for labelling (time, frequency of usage, region, language, subject, industry, and preferences) and the corresponding classification problems. To solve these problems automatically we sketch for each problem a pragmatic mix of machine learning methods and report selected results.

[16] Andreas Geyer-Schulz, Michael Hahsler, and Maximillian Jahn. myvu: A next generation recommender system based on observed consumer behavior and interactive evolutionary algorithms. In Wolfgang Gaul, Otto Opitz, and Martin Schader, editors, Data Analysis: Scientific Modeling and Practical Applications, Studies in Classification, Data Analysis, and Knowledge Organization, pages 447--457. Springer Verlag, Heidelberg, Germany, 2000. [ bib | at the publisher | .pdf ]
myVU is a next generation recommender system based on observed consumer behavior and interactive evolutionary algorithms implementing customer relationship management and one-to-one marketing in the educational and scientific broker system of a virtual university. myVU provides a personalized, adaptive WWW-based user interface for all members of a virtual university and it delivers routine recommendations for frequently used scientific and educational Web-sites.


Michael Hahsler, last modified