Benchmark f¨ur ein personalisiertes Empfehlungssystem mit zeitlicher Segmentierung basierend auf Assoziationsregeln

In der heutigen Zeit ist es ¨ublich geworden, im Online-Marketing Empfehlungssysteme einzusetzen. Im Folgenden wird ein Empfehlungsalgorithmus vorgestellt, der auf Basis von Kaufinformationen mit Hilfe von Assoziationsregeln und unter Ber¨ucksichtigung des Zeitpunkts des Kaufs personalisierte Produktempfehlungen generiert. Des Weiteren wird ein Benchmark mit dem vorgestellten, einem auf Assoziationsregeln basierenden und einem ItemKNN-Algorithmus unter Verwendung realer Kaufdaten erstellt. Die beiden zuletzt genannten Algorithmen wurden der Open-Source Bibliothek LibRec entnommen

Integration und Konnexion : Tagungsband zur 26. AKWI-Jahrestagung vom 15. bis 18.09.2013 an der Technischen Hochschule Mittelhessen

Das aufgerufene Thema „Herausforderungen an die Wirtschaftsinformatik: Integration und Konnexion“ provozierte Beiträge, die thematisch ein sehr breites Spektrum abdecken. Neben theoretischen Betrachtungen und Definitionen des sicher noch nicht final geprägten Begriffs der Konnexion gab es auch sehr praktische Beiträge wie die Darstellung von konkreten prototypischen Entwicklungsvorhaben. Auch das ist ein Indiz für die lebendige Landschaft der Wirtschaftsinformatik an den deutschsprachigen Hochschulen für Angewandte Wissenschaften

An empirical evaluation of a novel domain-specific language – modelling vehicle routing problems with Athos

Domain-specific languages (DSLs) are a popular approach among software engineers who demand for a tailored development interface. A DSL-based approach allows to encapsulate the intricacies of the target platform in transformations that turn DSL models into executable software code. Often, DSLs are even claimed to reduce development complexity to a level that allows them to be successfully applied by domain-experts with limited programming knowledge. Recent research has produced some scientifically backed insights on the benefits and limitations of DSLs. Further empirical studies are required to build a sufficient body of knowledge from which support for different claims related to DSLs can be derived. In this research study, we adopt current DSL evaluation approaches to investigate potential gains in terms of effectiveness and efficiency, through the application of our DSL Athos, a language developed for the domain of traffic and transportation simulation and optimisation. We compare Athos to the alternative of using an application library defined within a general-purpose language (GPL). We specified two sets of structurally identical tasks from the domain of vehicle routing problems and asked study groups with differing levels of programming knowledge to solve the tasks with the two approaches. The results show that inexperienced participants achieved considerable gains in effectiveness and efficiency with the usage of Athos DSL. Though hinting at Athos being the more efficient approach, the results were less distinct for more experienced programmers. The vast majority of participants stated to prefer working with Athos over the usage of the presented GPL’s API

An overview of agent-based traffic simulators

Individual traffic significantly contributes to climate change and environmental degradation. Therefore, innovation in sustainable mobility is gaining importance as it helps to reduce environmental pollution. However, effects of new ideas in mobility are difficult to estimate in advance and strongly depend on the individual traffic participants. The application of agent technology is particularly promising as it focuses on modelling heterogeneous individual preferences and behaviours. In this paper, we show how agent-based models are particularly suitable to address three pressing research topics in mobility: 1. Social dilemmas in resource utilisation; 2. Digital connectivity; and 3. New forms of mobility. We then explain how the features of several agent-based simulators are suitable for addressing these topics. We assess the capability of simulators to model individual travel behaviour, discussing implemented features and identifying gaps in functionality that we consider important

Trusting Intelligent Machines: Deepening Trust Within Socio-Technical Systems

Intelligent machines have reached capabilities that go beyond a level that a human being can fully comprehend without sufficiently detailed understanding of the underlying mechanisms. The choice of moves in the game Go (generated by Deep Mind?s Alpha Go Zero [1]) are an impressive example of an artificial intelligence system calculating results that even a human expert for the game can hardly retrace [2]. But this is, quite literally, a toy example. In reality, intelligent algorithms are encroaching more and more into our everyday lives, be it through algorithms that recommend products for us to buy, or whole systems such as driverless vehicles. We are delegating ever more aspects of our daily routines to machines, and this trend looks set to continue in the future. Indeed, continued economic growth is set to depend on it. The nature of human-computer interaction in the world that the digital transformation is creating will require (mutual) trust between humans and intelligent, or seemingly intelligent, machines. But what does it mean to trust an intelligent machine? How can trust be established between human societies and intelligent machines

Ontology Based Business Simulations

Within business games there is a need to provide realistic feedback for decisions made, if such business games are to continue to remain relevant in increasingly complex business environments. We address this problem by using soware agents to simulate individuals and to model their actions in response to business decisions. In our initial studies we have used soware agents to simulate consumers who make buying decisions based on their private preferences and those prevalent within their social network. This approach can be applied to search for behavioural patterns in social structures and to verify predicted values based on a priori theoretical considerations. Individual behaviour can be modelled for each agent and its eects within the marketplace can be examined by running simulations. Our simulations are founded upon the BDI soware model (belief-desire-intention) combined with ontologies to makeworld knowledge available to the agents which can then determine their actions in accordance with this knowledge. We demonstrate howontologies can be integrated into the BDI concept utilising the Jadex agent framework. Our examples are based upon the simulation of market mechanisms within the context of dierent industries. We use a framework, developed previously known as AGADE within which each agent evolves its knowledge using an ontology maintained during the simulation. This generic approach allows the simulation of various consumer scenarios which can be modelled by creating appropriate ontologies

A Conceptual Framework for Establishing Trust in Real World Intelligent Systems

Intelligent information systems that contain emergent elements often encounter trust problems because results do not get sufficiently explained and the procedure itself can not be fully retraced. This is caused by a control flow depending either on stochastic elements or on the structure and relevance of the input data. Trust in such algorithms can be established by letting users interact with the system so that they can explore results and find patterns that can be compared with their expected solution. Reflecting features and patterns of human understanding of a domain against algorithmic results can create awareness of such patterns and may increase the trust that a user has in the solution. If expectations are not met, close inspection can be used to decide whether a solution conforms to the expectations or whether it goes beyond the expected. By either accepting or rejecting a solution, the user's set of expectations evolves and a learning process for the users is established. In this paper we present a conceptual framework that reflects and supports this process. The framework is the result of an analysis of two exemplary case studies from two different disciplines with information systems that assist experts in their complex tasks