Recommending or persuading? The impact of a shopping agent's algorithm on user behavior

This paper investigates the potential of recommendation agents for electronic shopping to influence human decision making by shaping user preferences. Specifically, we examine how the type of information that is elicited by a shopping agent for use in its recommendation algorithm may affect consumers' preference for product features and ultimately their product choice in an electronic marketplace. A recommendation agent is defined as a software tool that (a) calibrates a model of a user's preference based on his/her input and (b) uses this model to make personalized product recommendations. We report the results of a controlled experiment that demonstrates that, everything else being equal, the inclusion of a product feature in a recommendation agent renders this feature more prominent in shoppers' purchase decisions. In addition, we find that this effect is moderated by an important property of the marketplace - the correlation structure among the features of available products. We conclude that electronic shopping agents, through the design of their recommendation algorithms, have the potential to influence user preferences in a systematic fashion.