this article, we propose to deal with this sparsity problem by applying an associative retrieval framework and related spreading activation algorithms to explore transitive associations among consumers through their past transactions and feedback. Such transitive associations are a valuable source of information to help infer consumer interests and can be explored to deal with the sparsity problem. To evaluate the effectiveness of our approach, we have conducted an experimental study using a data set from an online bookstore. We experimented with three spreading activation algorithms including a constrained Leaky Capacitor algorithm, a branch-and-bound serial symbolic search algorithm, and a Hopfield net parallel relaxation search algorithm. These algorithms were compared with several collaborative filtering approaches that do not consider the transitive associations: a simple graph search approach, two variations of the user-based approach, and an item-based approach. Our experimental results indicate that spreading activation-based approaches significantly outperformed the other collaborative filtering methods as measured by recommendation precision, recall, the F-measure, and the rank score. We also observed the over-activation effect of the spreading activation approach, that is, incorporating transitive associations with past transactional data that is not sparse may "dilute" the data used to infer user preferences and lead to degradation in recommendation performance

Abstract. Network-based marketing refers to a collection of marketing techniques that take advantage of links between consumers to increase sales. We concentrate on the consumer networks formed using direct interactions (e.g., communications) between consumers. We survey the diverse literature on such marketing with an emphasis on the statistical methods used and the data to which these methods have been applied. We also provide a discussion of challenges and opportunities for this burgeoning research topic. Our survey highlights a gap in the literature. Because of inadequate data, prior studies have not been able to provide direct, statistical support for the hypothesis that network linkage can directly affect product/service adoption. Using a new data set that represents the adoption of a new telecommunications service, we show very strong support for the hypothesis. Specifically, we show three main results: (1) “Network neighbors”—those consumers linked to a prior customer—adopt the service at a rate 3–5 times greater than baseline groups selected by the best practices of the firm’s marketing team. In addition, analyzing the network allows the firm to acquire new customers who otherwise would have fallen through the cracks, because they would not have been identified based on traditional attributes. (2) Statistical models, built with a very large amount of geographic, demographic and prior purchase data, are significantly and substantially improved by including network information. (3) More detailed network information allows the ranking of the network neighbors so as to permit the selection of small sets of individuals with very high probabilities of adoption. Key words and phrases: Viral marketing, word of mouth, targeted marketing, network analysis, classification, statistical relational learning. 1.

Recommender systems are being increasingly adopted in various e-commerce applications. A wide range of recommendation approaches have been developed to analyze past consumer-product interactions, consumer attributes, and product attributes to predict future sales. In this paper we propose a unified recommendation framework based on probabilistic relational models (PRMs). This framework includes most of existing recommendation approaches, such as collaborative filtering, content-based, demographic filtering, and hybrid approaches, as special cases. Recently developed in the machine learning community, PRMs aim to study the relational patterns within a database containing multiple interlinked data tables using a statistical model that describes probabilistic dependencies between attributes in the domain. We extended the original PRMs in order to capture relational data patterns that are important for recommendation. We also specialized the algorithm for learning PRMs in dependency model construction and parameter estimation to exploit the special characteristics of the recommendation problem. Through an experimental study, we demonstrate that the proposed framework not only conceptually unifies existing recommendation approaches but also allows the exploitation of a wider range of relational data patterns in an integrated manner, leading to improved recommendation performance. 1.

Customer modeling is a critical component of customer relationship management (CRM). Successful customer modeling requires a holistic view and the consolidation of all customer information available to the business, which is typically stored in a relational database. With this understanding, customer modeling in CRM can be viewed as a special case of the relational learning problem, a recent extension of the traditional machine learning problem that aims to model the relational interdependencies within a database containing multiple interlinked tables.

and Web usage for literature recommendation in digital libraries

Key words: collaborative filtering, maximum capacity path

Summary. Anonymous recommender systems are the electronic pendant to vendors, who ask the customers a few questions and subsequently recommend products based on the answers. In this article we will propose attribute aware classifier-based approaches for such a system and compare it to classifier-based approaches that only make use of the product IDs and to an existing knowledge-based system. We will show that the attribute-based model is very robust against noise and provides good results in a learning over time experiment. 1

In this paper we present a new method to compute frame based audio similarities, based on nearest neighbour density estimation. We do not recommend it is as a practical method for large collections because of the high runtime. Rather, we use this new method for a detailed analysis to get a deeper insight on how a bag of frames approach (BOF) determines similarities among songs, and in particular, to identify those audio frames that make two songs similar from a machine’s point of view. Our analysis reveals that audio frames of very low energy, which are of course not the most salient with respect to human perception, have a surprisingly big influence on current similarity measures. Based on this observation we propose to remove these low-energy frames before computing song models and show, via classification experiments, that the proposed frame selection strategy improves the audio similarity measure. 1.

der Lösungen, ihre Ausdehnung in immer neue Anwendungsdomänen sowie die zunehmende Verzahnung mit der operativen Prozessunterstützung werfen eine Vielzahl an neuen organisatorischen, betriebswirtschaftlichen und technischen Fragen auf, die sich mit dem verfügbaren konzeptionellen Instrumentarium bislang oftmals nur unbefriedigend abdecken lassen. Die deutschsprachige Wirtschaftsinformatik mit ihrer lösungsorientierten und interdisziplinären Ausrichtung sowie ihrer langen Tradition in der Erforschung von Management Support Systemen ist bestens positioniert, um sich den entsprechenden Herausforderungen zu stellen und mit wissenschaftlicher Rigorosität neue Perspektiven aufzuzeigen. Ziel des Kolloquiums war es, innovative Forschungsansätze und Forschungsergebnisse aus den Themenfeldern Business Intelligence (BI) und integrative Management Support Systeme (MSS) vorzustellen, zu diskutieren und zueinander in Beziehung zu setzen. Hierbei wurden bewusst auch Arbeiten in frühen Forschungsstadien berücksichtigt, um so Entwicklungslinien im Bereich der BI-Forschung aufzudecken, Kooperationspotentiale offenzulegen und für weitere Forschungsaktivitäten neue Impulse zu geben.

A collaborative filtering framework based on fuzzy association rules and multiple-level similarity