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@comment{{Command line: /usr/bin/bib2bib -c 'category : "recommender systems"' -ob recommender.bib hahsler.bib}}
@inproceedings{hahsler:Hahsler2005e,
author = {Michael Hahsler},
title = {Optimizing Web Sites for Customer Retention},
booktitle = {Proceedings of the 2005 International Workshop on Customer Relationship
Management: Data Mining Meets Marketing, November 18--19, 2005, New
York City, USA},
year = {2005},
editor = {Bing Liu and Myra Spiliopoulou and Jaideep Srivastava and Alex Tuzhilin},
abstract = {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.},
pdf = {http://michael.hahsler.net/research/LSD_CRM2005/LSD_CRM2005.pdf},
category = {marketing, recommender systems}
}
@incollection{hahsler:Geyer-Schulz2003e,
author = {Andreas Geyer-Schulz and Michael Hahsler},
title = {Comparing two Recommender Algorithms with the Help of Recommendations
by Peers},
booktitle = {WEBKDD 2002 - Mining Web Data for Discovering Usage Patterns and
Profiles 4th International Workshop, Edmonton, Canada, July 2002,
Revised Papers},
publisher = {Springer-Verlag},
year = {2003},
editor = {O.R. Zaiane and J. Srivastava and M. Spiliopoulou and B. Masand},
series = {Lecture Notes in Computer Science LNAI 2703},
pages = {137--158},
abstract = {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.},
note = {(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'')},
pdf = {http://michael.hahsler.net/research/recomm_lnai2002/lnai2002.pdf},
url = {http://www.springeronline.com/sgw/cda/frontpage/0,10735,5-146-22-14095354-0,00.html},
category = {recommender systems, association rules}
}
@incollection{hahsler:GeyerSchulz2003c,
author = {Andreas Geyer-Schulz and Michael Hahsler and Andreas Neumann and
Anke Thede},
title = {Behavior-Based Recommender Systems as Value-Added Services for Scientific
Libraries},
booktitle = {Statistical Data Mining \& Knowledge Discovery},
publisher = {Chapman \& Hall / CRC},
year = {2003},
editor = {Hamparsum Bozdogan},
pages = {433--454},
month = jul,
abstract = { 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 Universit{\"a}t Karlsruhe (TH) since January 2001 and performed
some diagnostic checks. The recommender service is fully operational
within the library system of the Universit{\"a}t Karlsruhe (TH) since
2002/06/22. },
url = {http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=C3448&parent_id=&pc=},
category = {recommender systems}
}
@inproceedings{hahsler:GeyerSchulz2003d,
author = {Andreas Geyer-Schulz and Michael Hahsler and Andreas Neumann and
Anke Thede},
title = {{Recommenderdienste f{\"u}r wissenschaftliche Bibliotheken und Bibliotheksverb{\"u}nde}},
booktitle = {Informationswirtschaft: Ein Sektor mit Zukunft, Symposium 4.--5.
September 2003, Wien, {{\"O}}sterreich},
year = {2003},
editor = {Andreas Geyer-Schulz and Alfred Taudes },
series = {Lecture Notes in Informatics (LNI) P-33},
pages = {43--58},
publisher = {Gesellschaft f{\"u}r Informatik},
abstract = {Wissenschaftliche Bibliotheken stellen ein vielversprechendes Anwendungsfeld
f{\"u}r Recommenderdienste dar. Wissenschaftliche Bibliotheken k{\"o}nnen
leicht kundenzentrierte Serviceportale im Stil von amazon.com entwickeln.
Studenten, Universit{\"a}tslehrer und -forscher k{\"o}nnen ihren
Anteil an den Transaktionskosten (z.B. Such- und Bewertungskosten
f{\"u}r Informationsprodukte) reduzieren. F{\"u}r Bibliothekare liegt
der Vorteil in einer Verbesserung der Kundenberatung durch Empfehlungen
und einer zus{\"a}tzlichen Unterst{\"u}tzung bei der Marktforschung,
Produktbewertung und dem Bestandsmanagement. In diesem Beitrag pr{\"a}sentieren
wir eine Strategie, mit der verhaltensbasierte, verteilte Recommenderdienste
in bestehende Bibliothekssysteme mit minimalem Aufwand integriert
werden k{\"o}nnen und berichten {\"u}ber unsere Erfahrungen bei der
Einf{\"u}hrung eines solchen Dienstes an der Universit{\"a}tsbibliothek
der Universit{\"a}t Karlsruhe (TH).},
url = {http://www.gi-ev.de/},
category = {recommender systems}
}
@inproceedings{hahsler:GeyerSchulz2003a,
author = {Andreas Geyer-Schulz and Michael Hahsler and Andreas Neumann and
Anke Thede},
title = {An Integration Strategy for Distributed Recommender Services in Legacy
Library Systems},
booktitle = {Between Data Science and Applied Data Analysis, Proceedings of the
26th Annual Conference of the Gesellschaft f{\"u}r Klassifikation
e.V., University of Mannheim, July 22--24, 2002},
year = {2003},
editor = {M. Schader and W. Gaul and M. Vichi},
series = {Studies in Classification, Data Analysis, and Knowledge Organization},
pages = {412--420},
month = jul,
publisher = {Springer-Verlag},
abstract = { 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. },
url = {http://www.springer.com/east/home/business/business+information+systems?SGWID=5-170-69-173622621-0},
category = {recommender systems}
}
@inproceedings{hahsler:GeyerSchulz2003b,
author = {Andreas Geyer-Schulz and Michael Hahsler and Anke Thede},
title = {Comparing association-rules and repeat-buying based recommender systems
in a {B2B} environment},
booktitle = {Between Data Science and Applied Data Analysis, Proceedings of the
26th Annual Conference of the Gesellschaft f{\"u}r Klassifikation
e.V., University of Mannheim, July 22--24, 2002},
year = {2003},
editor = {M. Schader and W. Gaul and M. Vichi},
series = {Studies in Classification, Data Analysis, and Knowledge Organization},
pages = {421--429},
month = jul,
publisher = {Springer-Verlag},
abstract = { 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. },
url = {http://www.springerlink.com/content/978-3-540-20304-9/},
category = {recommender systems}
}
@inproceedings{hahsler:GeyerSchulz2002,
author = {Walter B{\"o}hm and Andreas Geyer-Schulz and Michael Hahsler and
Maximillian Jahn},
title = {Repeat Buying Theory and its Application for Recommender Services},
booktitle = {{Exploratory Data Analysis in Empirical Research, Proceedings of
the 25th Annual Conference of the Gesellschaft f{\"u}r Klassifikation
e.V., University of Munich, March 14--16, 2001}},
year = {2002},
editor = {O. Opitz and M. Schwaiger},
pages = {229--239},
publisher = {Springer-Verlag},
abstract = {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.},
pdf = {http://michael.hahsler.net/research/recomm_gfkl2001/gfkl2001.pdf},
url = {http://www.springer.com/east/home/business/business+information+systems?SGWID=5-170-69-173622621-0},
category = {recommender systems}
}
@article{hahsler:GeyerSchulz2002a,
author = {Wolfgang Gaul and Andreas Geyer-Schulz and Michael Hahsler and Lars
Schmidt-Thieme},
title = {{eMarketing mittels Recommendersystemen}},
journal = {{Marketing ZFP}},
year = {2002},
volume = {24},
pages = {47--55},
abstract = {Recommendersysteme liefern einen wichtigen Beitrag f{\"u}r die Ausgestaltung
von eMarketing Aktivit{\"a}ten. 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
M{\"o}glichkeiten von Recommendersystemen verdeutlicht. Abschlie{\ss}end
wird zur Abrundung auf weitere Literatur mit Recommendersystem Bezug
eingegangen. In einem Ausblick werden Hinweise gegeben, in welche
Richtungen Weiterentwicklungen geplant sind.},
series = {Spezialausgabe ''E-Marketing''},
url = {http://vahlen.becksche.de/zeitschriften/},
category = {recommender systems, marketing}
}
@inproceedings{hahsler:GeyerSchulz2002d,
author = {Andreas Geyer-Schulz and Michael Hahsler},
title = {Evaluation of Recommender Algorithms for an Internet Information
Broker based on Simple Association Rules and on the Repeat-Buying
Theory},
booktitle = {Fourth WEBKDD Workshop: Web Mining for Usage Patterns \& User Profiles},
year = {2002},
editor = {Brij Masand and Myra Spiliopoulou and Jaideep Srivastava and Osmar
R. Zaiane},
pages = {100--114},
address = {Edmonton, Canada},
month = jul,
abstract = {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.},
pdf = {http://michael.hahsler.net/research/recomm_webkdd2002/final/webkdd2002.pdf},
category = {recommender systems, association rules}
}
@inproceedings{hahsler:GeyerSchulz2001,
author = {Andreas Geyer-Schulz and Michael Hahsler and Maximillian Jahn},
title = {Recommendations for Virtual Universities from Observed User Behavior},
booktitle = {Classification, Automation, and New Media, Proceedings of the 24th
Annual Conference of the Gesellschaft f{\"u}r Klassifikation e.V.,
University of Passau, March 15--17, 2000 },
year = {2002},
editor = {W. Gaul and G. Ritter},
pages = {273--280},
publisher = {Springer-Verlag},
abstract = { Recently recommender systems started to gain ground in commercial
Web-applications. For example, the online-bookseller {\em 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.},
pdf = {http://michael.hahsler.net/research/recomm_gfkl2000/paper.pdf},
url = {http://www.springer.com/east/home/business/business+information+systems?SGWID=5-170-69-173622621-0},
category = {recommender systems}
}
@incollection{hahsler:GeyerSchulz2002b,
author = {Andreas Geyer-Schulz and Michael Hahsler and Maximillian Jahn},
title = {A Customer Purchase Incidence Model Applied to Recommender Systems},
booktitle = {WEBKDD 2001 - Mining Log Data Across All Customer Touch Points, Third
International Workshop, San Francisco, CA, USA, August 26, 2001,
Revised Papers},
publisher = {Springer-Verlag},
year = {2002},
editor = {R. Kohavi and B.M. Masand and M. Spiliopoulou and J. Srivastava},
series = {Lecture Notes in Computer Science LNAI 2356},
pages = {25--47},
month = jul,
abstract = {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.},
note = {(Revised version of the WEBKDD 2001 paper ``A Customer Purchase
Incidence Model Applied to Recommender Systems'')},
pdf = {http://michael.hahsler.net/research/recomm_lncs2001/lncswebkdd2001a/lncswebkdd2001a.pdf},
url = {http://www.springerlink.com/content/mb2rqan13gy9/},
category = {recommender systems}
}
@inproceedings{hahsler:GeyerSchulz2001e,
author = {Andreas Geyer-Schulz and Michael Hahsler and Maximillian Jahn},
title = {{Wissenschaftliche Recommendersysteme in Virtuellen Universit{\"a}ten}},
booktitle = {Unternehmen Hochschule},
year = {2001},
editor = {H.-J. Appelrath and R. Beyer and U. Marquardt and H.C. Mayr and C.
Steinberger},
address = {Wien, {\"O}sterreich},
month = sep,
note = {Symposium UH2001, GI Lecture Notes in Informatics (LNI)},
abstract = { In diesem Beitrag wird die Rolle von Recommendersystemen und ihr
Potential in der Lehr-, Lern- und Forschungsumgebung einer Virtuellen
Universit{\"a}t untersucht.Die Hauptidee dieses Beitrags besteht
darin, die Informationsaggregationsf{\"a}higkeiten von Recommendersystemen
in einer Virtuellen Universit{\"a}t auszunutzen, um Tutoren-und Beratungsdienste
in einer Virtuellen Universit{\"a}t automatisch zu verbessern, um
damit Betreuung und Beratung von Studierenden zu personalisieren
und f{\"u}r eine gr{\"o}{\ss}ere Anzahl von Teilnehmern bei gleichzeitiger
Entlastung der Lehrenden verf{\"u}gbar zu machen. Im zweiten Teil
dieses Beitrags werden die Recommenderdienste von myVU, der Sammlung
der personalisierten Dienste der Virtuellen Universit{\"a}t (VU)
der Wirtschaftsuniversit{\"a}t Wien und ihre nicht-personalisierten
Variantenbeschrieben, die im Wesentlichen auf beobachtetem Benutzerverhalten
und, in der personalisierten Variante, zus{\"a}tzlich auf Selbstselektion
durch Selbsteinsch{\"a}tzung der Erfahrung in einem Fachgebiet beruhen.
Abschlie{\ss}end wird noch der innovative Einsatz solcher Systeme diskutiert
und an einigen Szenarien beschrieben. },
pdf = {http://michael.hahsler.net/research/unternehmenhochschule2001/uh2001.pdf},
url = {http://www.gi-ev.de/},
category = {recommender systems}
}
@article{hahsler:GeyerSchulz2001b,
author = {Andreas Geyer-Schulz and Michael Hahsler and Maximillian Jahn},
title = {Educational and Scientific Recommender Systems: Designing the Information
Channels of the Virtual University},
journal = {International Journal of Engineering Education},
year = {2001},
volume = {17},
pages = {153--163},
number = {2},
abstract = {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.},
issn = {0949-149X},
pdf = {http://michael.hahsler.net/research/recomm_ijee2001/paper.pdf},
series = {Special Issue on Virtual Universities},
url = {http://www.ijee.dit.ie/contents/c170201.html},
category = {recommender systems}
}
@inproceedings{hahsler:GeyerSchulz2001c,
author = {Andreas Geyer-Schulz and Michael Hahsler and Maximillian Jahn},
title = {A Customer Purchase Incidence Model Applied to Recommender Systems},
booktitle = {WEBKDD2001 Workshop: Mining Log Data Across All Customer TouchPoints},
year = {2001},
pages = {35--45},
address = {San Francisco, CA},
month = aug,
abstract = {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 },
pdf = {http://michael.hahsler.net/research/recomm_webKDD2001/paper/geyerschulz.pdf},
category = {recommender systems}
}
@inproceedings{hahsler:GeyerSchulz2000,
author = {Andreas Geyer-Schulz and Michael Hahsler},
title = {Automatic Labelling of References for Information Systems},
booktitle = {Classification and Information Processing at the Turn of the Millennium,
Proceedings of the 23rd Annual Conference of the Gesellschaft f{\"u}r
Klassifikation e.V., University of Bielefeld, March 10--12, 1999},
year = {2000},
editor = {Reinhold Decker and Wolfgang Gaul},
series = {Studies in Classification, Data Analysis, and Knowledge Organization},
pages = {451--459},
publisher = {Springer-Verlag},
abstract = {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.},
pdf = {http://michael.hahsler.net/research/labeling_gfkl1999/paper/labelling.pdf},
url = {http://www.springer.com/east/home/business/business+information+systems?SGWID=5-170-69-173622621-0},
category = {recommender systems}
}
@incollection{hahsler:GeyerSchulz2000a,
author = {Andreas Geyer-Schulz and Michael Hahsler and Maximillian Jahn},
title = {myVU: A Next Generation Recommender System Based on Observed Consumer
Behavior and Interactive Evolutionary Algorithms},
booktitle = {Data Analysis: Scientific Modeling and Practical Applications},
publisher = {Springer Verlag},
year = {2000},
editor = {Wolfgang Gaul and Otto Opitz and Martin Schader},
series = {Studies in Classification, Data Analysis, and Knowledge Organization},
pages = {447--457},
address = {Heidelberg, Germany},
abstract = {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.},
pdf = {http://michael.hahsler.net/research/festschrift2000/paper.pdf},
url = {http://www.springer.com/east/home/business/business+information+systems?SGWID=5-170-69-173622621-0},
category = {recommender systems}
}
This file was generated by bibtex2html 1.96.