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@inproceedings{hahsler:Kotamarti2010,
author = {Rao M Kotamarti and Michael Hahsler and Douglas W Raiford and Margaret H Dunham},
title = {Sequence transformation to a complex signature form for consistent Phylogetic tree using Extensible Markov Model},
booktitle = {Proceedings of the 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB 2010)},
year = {2010},
editor = {},
pages = {},
publisher = {IEEE},
abstract = {
Phylogenetic tree analysis using molecular sequences
continues to expand beyond the 16S rRNA marker. By addressing
the multi-copy issue known as the intra-heterogeneity,
this paper restores the focus in using the 16S rRNA marker.
Through use of a novel learning and model building algorithm,
the multiple gene copies are integrated into a compact complex
signature using the Extensible Markov Model (EMM). The
method clusters related sequence segments while preserving
their inherent order to create an EMM signature for a microbial
organism. A library of EMM signatures is generated
from which samples are drawn for phylogenetic analysis. By
matching the components of two signatures, referred to as
quasi-alignment, the differences are highlighted and scored.
Scoring quasi-alignments is done using adapted Karlin-Altschul
statistics to compute a novel distance metric. The metric satisfies
conditions of identity, symmetry, triangular inequality and the
four point rule required for a valid evolution distance metric.
The resulting distance matrix is input to PHYologeny Inference
Package (PHYLIP) to generate phylogenies using neighbor
joining algorithms. Through control of clustering in signature
creation, the diversity of similar organisms and their placement
in the phylogeny is explained. The experiments include analysis
of genus Burkholderia, a random microbial sample spanning
several phyla and a diverse sample that includes RNA of
Eukaryotic origin. The NCBI sequence data for 16S rRNA is
used for validation.
},
pdf = {http://michael.hahsler.net/research/EMMSA/EMMSA_CIBCB2010.pdf}
}
@inproceedings{hahsler:Hahsler2007b,
author = {Christoph Breidert and Michael Hahsler},
title = {Adaptive Conjoint Analysis for Pricing Music Downloads},
booktitle = {Advances in Data Analysis, Proceedings of the 30th Annual Conference
of the Gesellschaft f{\"u}r Klassifikation e.V., Freie Universit\"at
Berlin, March 8--10, 2006},
year = {2007},
pages = {409--416},
editor = {R. Decker and H.-J. Lenz},
series = {Studies in Classification, Data Analysis, and Knowledge Organization},
publisher = {Springer-Verlag},
abstract = {Finding the right pricing for music downloads is of ample importance
to the recording industry and music download service providers. For
the recently introduced music downloads, reference prices are still
developing and to find a revenue maximizing pricing scheme is a challenging
task. The most commonly used approach is to employ linear pricing
(e.g., iTunes, musicload). Lately, subscription models have emerged,
offering their customers unlimited access to streaming music for
a monthly fee (e.g., Napster, RealNetworks). However, other pricing
strategies could also be used, such as quantity rebates starting
at certain download volumes. Research has been done in this field
and Buxmann et al. (2005) have shown that price cuts can improve
revenue. In this paper we apply different approaches to estimate
consumer's willingness to pay (WTP) for music downloads and compare
our findings with the pricing strategies currently used in the market.
To make informed decisions about pricing, knowledge about the consumer's
WTP is essential. Three approaches based on adaptive conjoint analysis
to estimate the WTP for bundles of music downloads are compared.
Two of the approaches are based on a status-quo product (at market
price and alternatively at an individually self-stated price), the
third approach uses a linear model assuming a fixed utility per title.
All three methods seem to be robust and deliver reasonable estimations
of the respondent's WTPs. However, all but the linear model need
an externally set price for the status-quo product which can introduce
a bias.},
pdf = {http://michael.hahsler.net/research/conjoint_gfkl2006/conjoint_music.pdf},
url = {http://dx.doi.org/10.1007/978-3-540-70981-7}
}
@inproceedings{hahsler:Hahsler2007,
author = {Michael Hahsler and Kurt Hornik},
title = {Building on the arules Infrastructure for Analyzing Transaction Data
with {R}},
booktitle = {Advances in Data Analysis, Proceedings of the 30th Annual Conference
of the Gesellschaft f{\"u}r Klassifikation e.V., Freie Universit\"at
Berlin, March 8--10, 2006},
pages = {449--456},
year = {2007},
editor = {R. Decker and H.-J. Lenz},
series = {Studies in Classification, Data Analysis, and Knowledge Organization},
publisher = {Springer-Verlag},
abstract = {The free and extensible statistical computing environment R with its
enormous number of extension packages already provides many state-of-the-art
techniques for data analysis. Support for association rule mining,
a popular exploratory method which can be used, among other purposes,
for uncovering cross-selling opportunities in \emph{market baskets,}
has become available recently with the R extension package~arules.
After a brief introduction to transaction data and association rules,
we present the formal framework implemented in arules and demonstrate
how clustering and association rule mining can be applied together
using a market basket data set from a typical retailer. This paper
shows that implementing a basic infrastructure with formal classes
in R provides an extensible basis which can very efficiently be employed
for developing new applications (such as clustering transactions)
in addition to association rule mining.},
pdf = {http://michael.hahsler.net/research/arules_gfkl2006/arules_gfkl2006.pdf},
url = {http://dx.doi.org/10.1007/978-3-540-70981-7}
}
@inproceedings{hahsler:Hahsler2006b,
author = {Michael Hahsler and Kurt Hornik and Thomas Reutterer},
title = {Implications of Probabilistic Data Modeling for Mining Association
Rules},
booktitle = {From Data and Information Analysis to Knowledge Engineering, Proceedings
of the 29th Annual Conference of the Gesellschaft f{\"u}r Klassifikation
e.V., University of Magdeburg, March 9--11, 2005},
year = {2006},
editor = {M. Spiliopoulou and R. Kruse and C. Borgelt and A. N{\"u}rnberger
and W. Gaul},
series = {Studies in Classification, Data Analysis, and Knowledge Organization},
pages = {598--605},
publisher = {Springer-Verlag},
abstract = {Mining association rules is an important technique for discovering
meaningful patterns in transaction databases. In the current literature,
the properties of algorithms to mine association rules are discussed
in great detail. We present a simple probabilistic framework for
transaction data which can be used to simulate transaction data when
no associations are present. We use such data and a real-world grocery
database to explore the behavior of confidence and lift, two popular
interest measures used for rule mining. The results show that confidence
is systematically influenced by the frequency of the items in the
left-hand-side of rules and that lift performs poorly to filter random
noise in transaction data. The probabilistic data modeling approach
presented in this paper not only is a valuable framework to analyze
interest measures but also provides a starting point for further
research to develop new interest measures which are based on statistical
tests and geared towards the specific properties of transaction data.},
pdf = {http://michael.hahsler.net/research/probRuleMining_gfkl2005/probRuleMining_gfkl2005.pdf},
url = {http://www.springerlink.com/content/978-3-540-31314-4/}
}
@inproceedings{hahsler:Breidert2005,
author = {Christoph Breidert and Michael Hahsler and Lars Schmidt-Thieme},
title = {Reservation Price Estimation by Adaptive Conjoint Analysis},
booktitle = {Classification - the Ubiquitous Challenge, Proceedings of the 28th
Annual Conference of the Gesellschaft f{\"u}r Klassifikation e.V.,
University of Dortmund, March 9--11, 2004},
year = {2005},
editor = {Weihs, Claus and Gaul, Wolfgang},
series = {Studies in Classification, Data Analysis, and Knowledge Organization},
pages = {577--584},
publisher = {Springer-Verlag},
abstract = {Though reservation prices are needed for many business decision processes,
e.g., pricing new products, it often turns out to be difficult to
measure them. Many researchers reuse conjoint analysis data with
price as an attribute for this task (e.g., Kohli and Mahajan (1991)).
In this setting the information if a consumer buys a product at all
is not elicited which makes reservation price estimation impossible.
We propose an additional interview scene at the end of the adaptive
conjoint analysis (Johnson (1987)) to estimate reservation prices
for all product configurations. This will be achieved by the usage
of product stimuli as well as price scales that are adapted for each
proband to reflect individual choice behavior. We present preliminary
results from an ongoing large-sample conjoint interview of customers
of a major mobile phone retailer in Germany.},
pdf = {http://michael.hahsler.net/research/reservation_gfkl2004/gfkl2004.pdf},
url = {http://www.springerlink.com/content/978-3-540-28084-2/}
}
@inproceedings{hahsler:Fessler2005,
author = {Georg Fessler and Michael Hahsler and Michaela Putz},
title = {{ePubWU -- Erfahrungen mit einer Volltext an der Wirtschaftsuniversit{\"a}t
Wien}},
booktitle = {Bibliotheken -- Fundament der Bildung, 28. \"Osterreichischer Bibliothekartag
2004},
year = {2005},
editor = {Christian Enichlmayr},
series = {Schriftenreihe der O{\"o}. Landesbibliothek},
pages = {190--193},
abstract = {ePubWU ist eine elektronische Plattform f\"ur wissenschaftliche Publikationen
der Wirtschaftsuniversit\"at Wien, wo forschungsbezogene Ver\"offentlichungen
der WU im Volltext \"uber das WWW zug\"anglich gemacht werden. ePubWU
wird als Gemeinschaftsprojekt der Universit\"atsbibliothek der Wirtschaftsuniversit\"at
Wien und der Abteilung f\"ur Informationswirtschaft betrieben. Derzeit
werden in ePubWU zwei Publikationsarten gesammelt - Working Papers
und Dissertationen. In dem Beitrag werden Erfahrungen der \"uber zweij\"ahrigen
Laufzeit des Projektes dargestellt, u.a. in den Bereichen Akquisition,
Workflows, Erschlie{\ss}ung, Vermittlung.},
isbn = {3-85252-684-1}
}
@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}
}
@inproceedings{hahsler:Hahsler2005,
author = {Michael Hahsler and Stefan Koch},
title = {Discussion of a large-scale open source data collection methodology},
booktitle = {38th Annual Hawaii International Conference on System Sciences (HICSS'05),
January 3--6, 2005 Hilton Waikoloa Village, Big Island, Hawaii},
year = {2005},
publisher = {IEEE Computer Society Press},
abstract = { In this paper we discusses in detail a possible methodology for collecting
repository data on a large number of open source software projects
from a single project hosting and community site. The process of
data retrieval is described along with the possible metrics that
can be computed and which can be used for further analyses. Example
research areas to be addressed with the available data and first
results are given. Then, both advantages and disadvantages of the
proposed methodology are discussed together with implications for
future approaches.},
pdf = {http://michael.hahsler.net/research/oss_hicss2005/oss_hicss2005.pdf},
url = {http://csdl.computer.org/comp/proceedings/hicss/2005/2268/07/22680197babs.htm}
}
@inproceedings{hahsler:Hahsler2004b,
author = {Michael Hahsler and Stefan Koch},
title = {Cooperation and disruptive behaviour - Learning from a multi-player
Internet gaming community},
booktitle = {IADIS International Conference Web Based Communities 2004, Lisbon,
Portugal, 24--26 March 2004},
year = {2004},
editor = {Piet Kommers and Pedro Isaias and Miguel Baptista Nunes},
pages = {35--42},
publisher = {International Association for Development of the Information Society
(IADIS)},
abstract = { In this paper we report possibilities and experiences from employing
Counter-Strike, a popular multi-player Internet computer game and
its resulting online community in research on cooperative behaviour.
Advantages from using this game include easy availability of rich
data, the emphasis on team-playing, as well as numerous possibilities
to change the experiment settings. We use descriptive game theory
and statistical methods to explore cooperation within the game as
well as the way the player community deals with disruptive behaviour.
After a quick introduction to the basic rules of Counter-Strike,
we describe the setup of the Internet game server used. We then present
empirical results from the game server logs where cooperation within
the game is analyzed from a game theoretic perspective. Finally we
discuss the applications of our results to other online communities,
including cooperation and self-regulation in open source teams.},
pdf = {http://michael.hahsler.net/research/webBasedComm_cs/webBasedComm_cs.pdf},
url = {http://www.iadis.net/dl/Search_list_open.asp?code=730}
}
@inproceedings{hahsler:Bernroider2003a,
author = {Edward Bernroider and Michael Hahsler and Stefan Koch and Volker
Stix},
title = {{Data Envelopment Analysis zur Unterst{\"u}tzung der Auswahl und
Einf{\"u}hrung von ERP-Systemen}},
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 = {11--26},
publisher = {Gesellschaft f{\"u}r Informatik},
abstract = {Immer mehr Unternehmen setzen betriebswirtschaftliche Standardsoftwarepakete
wie beispielsweise SAP R/3 oder BaaN ein. Die Auswahl und die Einf{\"u}hrung
solcher Systeme stellt f{\"u}r die meisten Unternehmen ein strategisch
wichtiges IT-Projekt dar, das mit massiven Risiken verbunden ist.
Bei der Auswahl des am besten geeigneten Systems gilt es einen Gruppenentscheidungsprozess
zu unterst{\"u}tzen. Das darauf folgende Einf{\"u}hrungsprojekt muss
effizient, den ''best practices'' entsprechend, durchgef{\"u}hrt
werden. In dieser Arbeit wird anhand von Beispielen aufgezeigt, wie
beide Prozesse - die Auswahl und die Einf{\"u}hrung - durch die Data
Envelopment Analysis unterst{\"u}tzt werden k\"onnen.},
url = {http://www.gi-ev.de/}
}
@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/}
}
@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}
}
@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/}
}
@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}
}
@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}
}
@inproceedings{hahsler:GeyerSchulz2002c,
author = {Andreas Geyer-Schulz and Michael Hahsler},
title = {Software Reuse with Analysis Patterns},
booktitle = {Proceedings of the 8th AMCIS},
year = {2002},
pages = {1156--1165},
address = {Dallas, TX},
month = aug,
publisher = {Association for Information Systems},
abstract = {The purpose of this article is to promote reuse of domain knowledge
by introducing patterns already in the analysis phase of the software
life-cycle. We propose an outline template for analysis patterns
that strongly supports the whole analysis process from the requirements
analysis to the analysis model and further on to its transformation
into a flexible and reusable design and implementation. As an example
we develop a family of analysis patterns in this paper that deal
with a series of pressing problems in cooperative work, collaborative
information filtering and sharing, and knowledge management. We evaluate
the reuse potential of these patterns by analyzing several components
of an information system, that was developed for the Virtual University
project of the Vienna University of Economics and Business Administration.
The findings of this analysis suggest that using patterns in the
analysis phase has the potential to reducing development time significantly
by introducing reuse already at the analysis stage and by improving
the interface between analysis and design phase.},
pdf = {http://michael.hahsler.net/research/virlib_AMCIS2002/virlib_amcis2002.pdf},
url = {http://aisel.isworld.org/article_by_author.asp?Author_ID=86}
}
@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}
}
@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/}
}
@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}
}
@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}
}
@inproceedings{hahsler:GeyerSchulz2000c,
author = {Andreas Geyer-Schulz and Michael Hahsler},
title = {{Lebenslanges virtuelles Lernen}},
booktitle = {{Europas Arbeitswelt von Morgen}},
year = {2000},
editor = {Franciszek Grucza},
pages = {51--54},
address = {Wien},
publisher = {Wiener Zentrum der Polnischen Akademie der Wissenschaften},
url = {http://www.viennapan.org/06de01.htm}
}
@inproceedings{hahsler:Hahsler2000,
author = {Michael Hahsler and Bernd Simon},
title = {User-centered Navigation Re-Design for Web-based Information Systems},
booktitle = {Proceedings of the Sixth Americas Conference on Information Systems
(AMCIS 2000)},
year = {2000},
editor = {H. Michael Chung},
pages = {192--198},
address = {Long Beach, CA},
publisher = {Association for Information Systems},
abstract = {Navigation design for web-based information systems (e.g. e-commerce
sites, intranet solutions) that ignores user-participation reduces
the system's value and can even lead to system failure. In this paper
we introduce a user-centered, explorative approach to re-designing
navigation structures of web-based information systems, and describe
how it can be implemented in order to provide flexibility and reduce
maintenance costs. We conclude with lessons learned from the navigation
re-design project at the Vienna University of Economics and Business
Administration.},
pdf = {http://michael.hahsler.net/research/webdesign_amcis2000/TT04-11_final.pdf},
url = {http://aisel.isworld.org/article_by_author.asp?Author_ID=86}
}
@inproceedings{hahsler:GeyerSchulz1999b,
author = {Andreas Geyer-Schulz and Michael Hahsler and Georg Schneider},
title = {The Virtual University as a Network Economy},
booktitle = {Informatik '99, Unternehmen Hochschule '99, Workshop-Unterlagen},
year = {1999},
editor = {Heinrich C. Mayr and Claudia Steinberger and Hans-J{\"u}rgen Appelrath
and Uwe Marquardt},
pages = {75--86},
address = {Bielefeld, Germany},
month = oct
}
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