Due to the change in attitudes and lifestyles, people expect to find new partners and friends via various ways now-a-days. Online dating networks create a network for people to meet each other and allow making contact with different objectives of developing a personal, romantic or sexual...

Intelligent Product Recommendation Agents have been used for some time now by large, well known Internet businesses such as Amazon and Netflix. Unfortunately there is little research assessing the effectiveness of these systems in influencing online consumer behavior. Businesses that sell...

The explosive growth of information asks for advanced information filtering techniques to solve the so-called information overload problem. A promising way is the recommender system which analyzes the historical records of users’ activities and accordingly provides personalized...

In this paper, we introduce a modified collaborative filtering (MCF) algorithm, which has remarkably higher accuracy than the standard collaborative filtering. In the MCF, instead of the cosine similarity index, the user–user correlations are obtained by a diffusion process. Furthermore, by...

This paper demonstrates how image content can be used to realize a location-based shopping recommender system for intuitively supporting mobile users in decision making. Generic Fourier Descriptors (GFD) image content of an item was extracted to exploit knowledge contained in item and user...

Corporate portals have become an important component in company intranets. This work focuses on the challenges that arise due to constant evolution of such portals. A concept framework is presented to cope with these challenges. As part of this framework the implementation of a recommender...

In content- and knowledge-based recommender systems often a measure of (dis)similarity between products is used. Frequently, this measure is based on the attributes of the products. However, which attributes are important for the users of the system remains an important question to answer. In...

We conduct a field experiment on a movie-recommendation platform to identify if and how recommendations affect consumption. We use within-consumer randomization at the good level and elicit beliefs about unconsumed goods to disentangle exposure from informational effects. We find recommendations...

Algorithm-based decision support systems play an increasingly important role in decisions involving exploration tasks, such as product searches, portfolio choices, and human resource procurement. These tasks often involve a trade-off between exploration and exploitation, which can be highly...

Mit dem pandemiebedingten ersten Lockdown im März 2020 hat das digitale Lernen einen enormen Aufschwung erfahren. Mit der Initiative "School to go" wird in Zeiten bundesweiter Schulschließungen ein relevanter Beitrag zur digitalen Bildung für Schülerinnen und Schüler, Eltern, Lehrkräfte...