Publications: Michael Hahsler

Mining association rules is a popular and well researched method for discovering interesting relations between variables in large databases. A practical problem is that at medium to low support values often a large number of frequent itemsets and an even larger number of association rules are found in a database. A widely used approach is to gradually increase minimum support and minimum confidence or to filter the found rules using increasingly strict constraints on additional measures of interestingness until the set of rules found is reduced to a manageable size. In this paper we describe a different approach which is based on the idea to first define a set of “interesting” itemsets (e.g., by a mixture of mining and expert knowledge) and then, in a second step to selectively generate rules for only these itemsets. The main advantage of this approach over increasing thresholds or filtering rules is that the number of rules found is significantly reduced while at the same time it is not necessary to increase the support and confidence thresholds which might lead to missing important information in the database.

Seriation, i.e., finding a linear order for a set of objects given data and a loss or merit function, is a basic problem in data analysis. Caused by the problem's combinatorial nature, it is hard to solve for all but very small sets. Nevertheless, both exact solution methods and heuristics are available. In this paper we present the package seriation which provides the infrastructure for seriation with R. The infrastructure comprises data structures to represent linear orders as permutation vectors, a wide array of seriation methods using a consistent interface, a method to calculate the value of various loss and merit functions, and several visualization techniques which build on seriation. To illustrate how easily the package can be applied for a variety of applications, a comprehensive collection of examples is presented.

The traveling salesperson (or, salesman) problem (TSP) is a well known and important combinatorial optimization problem. The goal is to find the shortest tour that visits each city in a given list exactly once and then returns to the starting city. Despite this simple problem statement, solving the TSP is difficult since it belongs to the class of NP-complete problems. The importance of the TSP arises besides from its theoretical appeal from the variety of its applications. Typical applications in operations research include vehicle routing, computer wiring, cutting wallpaper and job sequencing. The main application in statistics is combinatorial data analysis, e.g., reordering rows and columns of data matrices or identifying clusters. In this paper we introduce the R package TSP which provides a basic infrastructure for handling and solving the traveling salesperson problem. The package features S3 classes for specifying a TSP and its (possibly optimal) solution as well as several heuristics to find good solutions. In addition, it provides an interface to Concorde, one of the best exact TSP solvers currently available.

Mining association rules is an important technique for discovering meaningful patterns in transaction databases. Many different measures of interestingness have been proposed for association rules. However, these measures fail to take the probabilistic properties of the mined data into account. In this paper, we start with presenting 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 database from a grocery outlet 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. Based on the probabilistic framework we develop two new interest measures, hyper-lift and hyper-confidence, which can be used to filter or order mined association rules. The new measures show significantly better performance than lift for applications where spurious rules are problematic.

Techniken des Data Mining stellen für die Marketingforschung und -praxis eine zunehmend bedeutsamere Bereicherung des herkömmlichen Methodenarsenals dar. Mit dem Einsatz solcher primär datengetriebener Analysewerkzeuge wird das Ziel verfolgt, marketingrelevante Informationen ”intelligent” aus großen Datenbanken (sog. Data Warehouses) zu extrahieren und für die weitere Entscheidungsvorbereitung in geeigneter Form aufzubereiten. Im vorliegenden Beitrag werden Berührungspunkte zwischen Data Mining und Marketing diskutiert und der konkrete Einsatz ausgewählter Data-Mining-Methoden am Beispiel der explorativen Warenkorb- bzw. Sortimentsverbundanalyse für einen Transaktionsdatensatz aus dem Lebensmitteleinzelhandel demonstriert. Zur Anwendung gelangen dabei Techniken aus dem Bereich der klassischen Affinitätsanalyse, ein K-Medoid-Verfahren der Clusteranalyse sowie Werkzeuge zur Generierung und anschließenden Beurteilung von Assoziationsregeln zwischen im Sortiment enthaltenen Warengruppen. Die Vorgehensweise wird dabei anhand des mit der Statistik-Software R frei verfügbaren Erweiterungspakets arules illustriert.

Mining frequent itemsets is a popular method for finding associated items in databases. For this method, support, the co-occurrence frequency of the items which form an association, is used as the primary indicator of the associations's significance. A single user-specified support threshold is used to decided if associations should be further investigated. Support has some known problems with rare items, favors shorter itemsets and sometimes produces misleading associations. In this paper we develop a novel model-based frequency constraint as an alternative to a single, user-specified minimum support. The constraint utilizes knowledge of the process generating transaction data by applying a simple stochastic mixture model (the NB model) which allows for transaction data's typically highly skewed item frequency distribution. A user-specified precision threshold is used together with the model to find local frequency thresholds for groups of itemsets. Based on the constraint we develop the notion of NB-frequent itemsets and adapt a mining algorithm to find all NB-frequent itemsets in a database. In experiments with publicly available transaction databases we show that the new constraint provides improvements over a single minimum support threshold and that the precision threshold is more robust and easier to set and interpret by the user.

Knowledge about a product's willingness-to-pay on behalf of its (potential) customers plays a crucial role in many areas of marketing management like pricing decisions or new product development. Numerous approaches to measure willingness-to-pay with differential conceptual foundations and methodological implications have been presented in the relevant literature so far. This article provides the reader with a systematic overview of the relevant literature on these competing approaches and associated schools of thought, recognizes their respective merits and discusses obstacles and issues regarding their adoption to measuring willingness-to-pay. Because of its practical relevance, special focus will be put on indirect surveying techniques and, in particular, conjoint-based applications will be discussed in more detail. The strengths and limitations of the individual approaches are discussed and evaluated from a managerial point of view.

Mining frequent itemsets and association rules is a popular and well researched approach for discovering interesting relationships between variables in large databases. The R package arules presented in this paper provides a basic infrastructure for creating and manipulating input data sets and for analyzing the resulting itemsets and rules. The package also includes interfaces to two fast mining algorithms, the popular C implementations of Apriori and Eclat by Christian Borgelt. These algorithms can be used to mine frequent itemsets, maximal frequent itemsets, closed frequent itemsets and association rules.

In this article we report on the effort of the university library of the Vienna University of Economics and Business Administration to integrate a digital library component for research documents authored at the university into the existing library infrastructure. Setting up a digital library has become a relatively easy task using the current data base technology and the components and tools freely available. However, to integrate such a digital library into existing library systems and to adapt existing document acquisition work-flows in the organization are non-trivial tasks. We use a research frame work to identify the key players in this change process and to analyze their incentive structures. Then we describe the light-weight integration approach employed by our university and show how it provides incentives to the key players and at the same time requires only minimal adaptation of the organization in terms of changing existing work-flows. Our experience suggests that this light-weight integration offers a cost efficient and low risk intermediate step towards switching to exclusive digital document acquisition.

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.

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.

In this article we present the current state of usage of (intelligent) Internet agents in the Virtual University (VU) of the Vienna University of Economics and BA. We discuss opportunities and challenges for the development of several classes of agents and their sensor systems. More specifically, agents of the following classes embedded in the virtual university system will be presented: (1) robots which support navigation services and (2) robots which support communication and collaboration.

In this paper we report on the status quo of the current machine learning research projects at the Department of Applied Computer Science of the Institute of Information Processing and Information Economics of the Vienna University of Economics and Business Administration. The current research activities can be categorized as follows: (1) Development of a theoretic framework of genetic programming. (2) Application of genetic programming for managerial and economic decision-making and for breeding agents' strategies in organizational learning. (3) Development, adaptation, and integration of (intelligent) Internet agents for support of the virtual organizations. (4) Development of an infrastructure for intelligent Internet agents in the ”Living Lectures - Virtual University” project. (5) Cost-benefit analysis of agents, analysis of tactical and strategic consequences of agents and the analysis of their economic applications.

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 näher beleuchtet, welche eine Verdichtung und kompakte Darstellung der in (zumeist sehr umfangreichen) Transaktionsdaten des Einzelhandels auffindbaren Verbundbeziehungen beabsichtigt. Mit einer enormen Anzahl an verfügbaren Erweiterungspaketen bietet sich die frei verfügbare Statistik-Software R als ideale Basis für die Durchführung solcher Warenkorbanalysen an. Die im Erweiterungspaket arules vorhandene Infrastruktur für Transaktionsdaten stellt eine flexible Basis für die Warenkorbanalyse bereit. Unterstützt 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 ermöglicht dadurch die direkte Anwendung von bereits vorhandenen modernsten Verfahren für Sampling, Clusterbildung und Visualisierung von Warenkorbdaten. Zusätzlich sind in arules gängige 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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

ePubWU ist eine elektronische Plattform für wissenschaftliche Publikationen der Wirtschaftsuniversität Wien, wo forschungsbezogene Veröffentlichungen der WU im Volltext über das WWW zugänglich gemacht werden. ePubWU wird als Gemeinschaftsprojekt der Universitätsbibliothek der Wirtschaftsuniversität Wien und der Abteilung für Informationswirtschaft betrieben. Derzeit werden in ePubWU zwei Publikationsarten gesammelt - Working Papers und Dissertationen. In dem Beitrag werden Erfahrungen der über zweijährigen Laufzeit des Projektes dargestellt, u.a. in den Bereichen Akquisition, Workflows, Erschließung, Vermittlung.

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.

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.

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.

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.

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.

Immer mehr Unternehmen setzen betriebswirtschaftliche Standardsoftwarepakete wie beispielsweise SAP R/3 oder BaaN ein. Die Auswahl und die Einführung solcher Systeme stellt für 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 unterstützen. Das darauf folgende Einführungsprojekt muss effizient, den ”best practices” entsprechend, durchgeführt werden. In dieser Arbeit wird anhand von Beispielen aufgezeigt, wie beide Prozesse - die Auswahl und die Einführung - durch die Data Envelopment Analysis unterstützt werden können.

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).

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.

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.

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.

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.

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.

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

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.

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.

Genome databases continue to expand with no change in the basic format of sequence data. The prevalent use of the classic alignment based search tools like BLAST have significantly pushed the limits of genome isolate research. The relatively new frontier of Metagenomic research deals with thousands of diverse genomes with newer demands beyond the current homologue search and analysis. Compressing sequence data into a complex form could facilitate a broader range of sequence analyses. To this end, this research explores reorganizing sequence data as complex Markov signatures also known as Extensible Markov Models. Markov models have found successful application in biological sequence analysis applications through small, but important extensions to the original theory of Markov Chains. Extensible Markov Model (EMM) offers a novel Quasi-alignment complement to the classic alignment based homologous sequence search methods like BLAST. EMM based bioinformatic analysis (EMMBA) incorporates automatic learning which allows the Markov chain creation dynamically. Oligonucletide or genomic word frequencies form the core sequence data in alignment free methods. EMMBA extends the Karlin-Altschul statistics to bring forth an analogous E-Score statistical significance to the quasi-alignment domain. By consolidating a community of sequences into a single searchable profile, EMM methodology further reduces the search space for classification. Through dynamic generation of the score matrix for each community profile, EMMBA fine tunes the score assignments. Each evaluation iteratively adjusts the profile score matrix to account for point probabilities of the query to ensure Karlin-Altschul assumptions are satisfied to derive meaningful statistical signifi- cance. The presence of multiple quasi-alignments resembles multiple local alignments of BLAST. Quasi-alignments are scored based on a difference distribution of Gumbel scores. Species signature profiles allow for statistical validation of novel species identification. Working in EMM transformation space speeds up classification and generates distance matrix for differentiation. The techniques and metrics presented are validated using the microbial 16s rRNA sequence data from NCBI.

For hierarchical clustering, dendrograms provide convenient and powerful visualization. Although many visualization methods have been suggested for partitional clustering, their usefulness deteriorates quickly with increasing dimensionality of the data and/or they fail to represent structure between and within clusters simultaneously. In this paper we extend (dissimilarity) matrix shading with several reordering steps based on seriation. Both methods, matrix shading and seriation, have been well-known for a long time. However, only recent algorithmic improvements allow to use seriation for larger problems. Furthermore, seriation is used in a novel stepwise process (within each cluster and between clusters) which leads to a visualization technique that is independent of the dimensionality of the data. A big advantage is that it presents the structure between clusters and the micro-structure within clusters in one concise plot. This not only allows for judging cluster quality but also makes mis-specification of the number of clusters apparent. We give a detailed discussion of the construction of dissimilarity plots and demonstrate their usefulness with several examples.

Seriation, i.e., finding a linear order for a set of objects given data and a loss or merit function, is a basic problem in data analysis. Caused by the problem's combinatorial nature, it is hard to solve for all but very small sets. Nevertheless, both exact solution methods and heuristics are available. In this paper we present the package seriation which provides the infrastructure for seriation with R. The infrastructure comprises data structures to represent linear orders as permutation vectors, a wide array of seriation methods using a consistent interface, a method to calculate the value of various loss and merit functions, and several visualization techniques which build on seriation. To illustrate how easily the package can be applied for a variety of applications, a comprehensive collection of examples is presented.

The traveling salesperson or salesman problem (TSP) is a well known and important combinatorial optimization problem. The goal is to find the shortest tour that visits each city in a given list exactly once and then returns to the starting city. Despite this simple problem statement, solving the TSP is difficult since it belongs to the class of NP-complete problems. The importance of the TSP arises besides from its theoretical appeal from the variety of its applications. In addition to vehicle routing, many other applications, e.g., computer wiring, cutting wallpaper, job sequencing or several data visualization techniques, require the solution of a TSP. In this paper we introduce the R package TSP which provides a basic infrastructure for handling and solving the traveling salesperson problem. The package features S3 classes for specifying a TSP and its (possibly optimal) solution as well as several heuristics to find good solutions. In addition, it provides an interface to Concorde, one of the best exact TSP solvers currently available.

Mining association rules is an important technique for discovering meaningful patterns in transaction databases. Many different measures of interestingness have been proposed for association rules. However, these measures fail to take the probabilistic properties of the mined data into account. In this paper, we start with presenting 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 database from a grocery outlet 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. Based on the probabilistic framework we develop two new interest measures, hyper-lift and hyper-confidence, which can be used to filter or order mined association rules. The new measures show significant better performance than lift for applications where spurious rules are problematic.

Mining frequent itemsets and association rules is a popular and well researched approach to discovering interesting relationships between variables in large databases. The R package arules presented in this paper provides a basic infrastructure for creating and manipulating input data sets and for analyzing the resulting itemsets and rules. The package also includes interfaces to two fast mining algorithms, the popular C implementations of Apriori and Eclat by Christian Borgelt. These algorithms can be used to mine frequent itemsets, maximal frequent itemsets, closed frequent itemsets and association rules.

Mining association rules is an important technique for discovering meaningful patterns in transaction databases. In the current literature, the properties of algorithms to mine associations are discussed in great detail. In this paper we investigate properties of transaction data sets from a probabilistic point of view. We present a simple probabilistic framework for transaction data and its implementation using the R statistical computing environment. The framework can be used to simulate transaction data when no associations are present. We use such data to explore the ability to filter noise of confidence and lift, two popular interest measures used for rule mining. Based on the framework we develop the measure hyperlift and we compare this new measure to lift using simulated data and a real-world grocery database.

In this paper we develop an alternative to minimum support which utilizes knowledge of the process which generates transaction data and allows for highly skewed frequency distributions. We apply a simple stochastic model (the NB model), which is known for its usefulness to describe item occurrences in transaction data, to develop a frequency constraint. This model-based frequency constraint is used together with a precision threshold to find individual support thresholds for groups of associations. We develop the notion of NB-frequent itemsets and present two mining algorithms which find all NB-frequent itemsets in a database. In experiments with publicly available transaction databases we show that the new constraint can provide significant improvements over a single minimum support threshold and that the precision threshold is easier to use.

Die vorliegende Arbeit beschäftigt sich mit dem Preisvergleich zwischen Online-Shops und traditionellen Geschäften. In einigen Studien wurde bisher versucht Preisunterschiede zwischen online und traditionellen Geschäften nachzuweisen, um die These, dass Online-Märkte aufgrund höherer Transparenz und niedrigerer Transaktionskosten effizienter sind, zu bestätigen. Studien untersuchten bisher Produktgruppen wie CDs und Bücher. In dieser Studie beschäftigen wir uns mit dem bisher noch nicht untersuchten Spieleeinzelhandel und konzentrieren uns dabei auf den österreichischen Markt. Es soll untersucht werden, ob der österreichische Markt ähnliche oder andere Ergebnisse liefert als die bisher untersuchten Märkte (hauptsächlich im nordamerikanischer Raum). Die Untersuchung zeigt folgendes: Die Preise für Spiele sind im elektronischen Markt um ca. 20 Prozent niedriger als im traditionellen Markt. Die Preisstreuungen im elektronischen und traditionellen Markt unterscheiden sich nicht signifikant. Beide Ergebnisse decken sich mit den Ergebnissen anderer Studien. Damit ist der österreichische Online-Brettspieleinzelhandel ähnlich entwickelt wie der Online-Handel in anderen Ländern und für andere Produktgruppen.

ePubWU ist eine elektronische Plattform für wissenschaftliche Publikationen der Wirtschaftsuniversität Wien, wo forschungsbezogene Veröffentlichungen der WU im Volltext über das WWW zugänglich gemacht werden. ePubWU ist seit Jänner 2002 im Echtbetrieb und wird als Gemeinschaftsprojekt der Universitätsbibliothek der Wirtschaftsuniversität Wien und der Abteilung für Informationswirtschaft betrieben. Dieser Bericht beinhaltet die Erfahrungen aus der 2-jährigen Pilotphase des Projekts.

Using design patterns is a widely accepted method to improve software development. There are many benefits of the application of patterns claimed in the literature. The most cited claim is that design patterns can provide a common design vocabulary and therefore improve greatly communication between software designers. Most of the claims are supported by experiences reports of practitioners, but there is a lack of quantitative research concerning the actual application of design patterns and about the realization of the claimed benefits. In this paper we analyze the development process of over 1000 open source software projects using version control information. We explore this information to gain an insight into the differences of software development with and without design patterns. By analyzing these differences we provide evidence that design patterns are used for communication and that there is a significant difference between developers who use design patterns and who do not.

The purpose of this article is twofold, first to promote the use of patterns in the analysis phase of the software life-cycle by proposing 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 design. Second we present, as an example, a family of analysis patterns that deal with a series of pressing problems in cooperative work, collaborative information filtering and sharing, and knowledge management. We present the step-by-step evolution of the analysis pattern virtual library with active agents starting with a simple pinboard.

Diese Arbeit beschäftigt sich mit Analyse Patterns, der Anwendung von Patterns in der Analysephase der Softwareentwicklung. In der Designphase werden Patterns seit einigen Jahren eingesetzt, um Expertenwissen und Wiederverwendbarkeit in den Designprozeß einfließen zu lassen. Es existiert bereits eine Fülle an solchen Design Patterns. Die Analysephase ist ein neuer Anwendungsbereich für Patterns, der bisher in der Literatur noch nicht ausreichend behandelt wurde. In dieser Arbeit wird die Anwendung des Pattern-Ansatzes in der Analysephase aufgearbeitet und konkretisiert. Analyse Patterns unterstützen den gesamten Softwareentwicklungsprozeß und helfen bekannte Probleme während der Analysephase zu lösen. Dadurch können Zeit und Kosten bei der Entwicklung neuer Softwaresysteme eingespart werden. Diese Eigenschaften von Analyse Patterns werden anhand konkreter Beispiele in einer Case Study nachgewiesen. Diese Case Study beschreibt den Einsatz von in dieser Arbeit entwickelten Analyse Pattern für Informationsmanagement anhand des Projekts Virtuelle Universität der Wirtschaftsuniversität Wien, in dem ein Internet-Informationsbroker zur Unterstützung von Lehre und Forschung realisiert wird. Die Erfahrungen aus diesem Projekt werden untersucht, und die Auswirkungen der Analyse Patterns auf Wiederverwendung bei der Softwareentwicklung und auf die Akzeptanz des resultierenden Systems werden präsentiert.

Diese Arbeit beschäftigt sich mit dem Pattern-Ansatz für die Architektur von Software. Nach einer kurzen Darstellung des Ansatzes werden das Pinwand-Pattern und seine Varianten beschrieben. Pinwände werden verwendet, um Informationen zu sammeln und Interessierten zur Verfügung zu stellen. Sie finden unter anderem in den folgenden Bereichen Anwendung: Groupware-Anwendungen, Conferencing Systeme, Diskussionsforen und Virtuelle Bibliotheken.