We develop a general agent-based modeling and computational simulation approach to study the impact of various factors on the temporal dynamics of recommender systems' performance. The proposed agent-based simulation approach allows for comprehensive analysis of longitudinal recommender systems...

Recommender systems are an integral part of the online retail environment. Prior research has focused largely on computational approaches to improving recommendation accuracy, and only recently researchers have started to study their behavioral implications and potential side effects. We used...

Recommender systems are becoming a salient part of many e-commerce websites. Much research has focused on advancing recommendation technologies to improve accuracy of predictions, while behavioral aspects of using recommender systems are often overlooked. In our studies, we explore how consumer...

Prior research has shown that online recommendations have a significant influence on consumers’ preference ratings and economic behavior. Specifically, biases induced by observing personalized system recommendations can lead to distortions in users’ self-reported preference ratings after...

We study the problem of consumer acquisition in recommender systems, by considering a marketplace where a recommender system provider offers personalized incentives to encourage consumer participation. We investigate a model of consumer acquisition that accounts for several key market dynamics,...