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@incollection{hahsler:Hahsler2006f,
author = {Michael Hahsler and Kurt Hornik and Thomas Reutterer},
title = {{Warenkorbanalyse mit Hilfe der Statistik-Software R}},
booktitle = {Innovationen in Marketing},
year = {2006},
editor = {Peter Schnedlitz and Renate Buber and Thomas Reutterer and Arnold
Schuh and Christoph Teller},
pages = {144--163},
publisher = {Linde-Verlag},
abstract = {Die Warenkorb- oder Sortimentsverbundanalyse bezeichnet eine Reihe
von Methoden zur Untersuchung der bei einem Einkauf gemeinsam nachgefragten
Produkte oder Kategorien aus einem Handelssortiment. In diesem Beitrag
wird die explorative Warenkorbanalyse n{\"a}her beleuchtet, welche eine
Verdichtung und kompakte Darstellung der in (zumeist sehr umfangreichen)
Transaktionsdaten des Einzelhandels auffindbaren Verbundbeziehungen
beabsichtigt. Mit einer enormen Anzahl an verf{\"u}gbaren Erweiterungspaketen
bietet sich die frei verf{\"u}gbare Statistik-Software R als ideale Basis
f{\"u}r die Durchf{\"u}hrung solcher Warenkorbanalysen an. Die im Erweiterungspaket
arules vorhandene Infrastruktur f{\"u}r Transaktionsdaten stellt eine
flexible Basis f{\"u}r die Warenkorbanalyse bereit. Unterst{\"u}tzt wird
die effiziente Darstellung, Bearbeitung und Analyse von Warenkorbdaten
mitsamt beliebigen Zusatzinformationen zu Produkten (zum Beispiel
Sortimentshierarchie) und zu Transaktionen (zum Beispiel Umsatz oder
Deckungsbeitrag). Das Paket ist nahtlos in R integriert und erm{\"o}glicht
dadurch die direkte Anwendung von bereits vorhandenen modernsten
Verfahren f{\"u}r Sampling, Clusterbildung und Visualisierung von Warenkorbdaten.
Zus{\"a}tzlich sind in arules g{\"a}ngige Algorithmen zum Auffinden von Assoziationsregeln
und die notwendigen Datenstrukturen zur Analyse von Mustern vorhanden.
Eine Auswahl der wichtigsten Funktionen wird anhand eines realen
Transaktionsdatensatzes aus dem Lebensmitteleinzelhandel demonstriert.},
pdf = {http://michael.hahsler.net/research/arules_WUCompDay2006/arules.pdf},
url = {http://www.lindeverlag.at/verlag/buecher/978-3-7143-0080-2}
}
@incollection{hahsler:Hahsler2004a,
author = {Michael Hahsler},
title = {A Quantitative Study of the Adoption of Design Patterns by Open Source
Software Developers},
booktitle = {Free/Open Source Software Development},
publisher = {Idea Group Publishing},
year = {2005},
editor = {S. Koch},
pages = {103--123},
abstract = {Several successful projects (Linux, Free-BSD, BIND, Apache, etc.)
showed that the collaborative and self-organizing process of developing
open source software produces reliable, high quality software. Without
doubt, the open source software development process differs in many
ways from the traditional development process in a commercial environment.
An interesting research question is how these differences influence
the adoption of traditional software engineering practices. In this
chapter we investigate how design patterns, a widely accepted software
engineering practice, are adopted by open source developers for documenting
changes. We analyze the development process of almost 1,000 open
source software projects using version control information and explore
differences in pattern adoption using characteristics of projects
and developers. By analyzing these differences we provide evidence
that design patterns are an important practice in open source projects
and that there exist significant differences between developers who
use design patterns and who do not.},
pdf = {http://michael.hahsler.net/research/patterns_oss2004/OSS_patterns_preprint.pdf},
url = {http://www.idea-group.com/books/details.asp?id=4368}
}
@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}
}
@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=}
}
@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/}
}
@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}
}
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