Transaction-driven personalization: The moderating effects of personality traits

Abstract

With transaction-driven personalization engines online merchants can use knowledge gained from an individual customer's past transactions to match web content to the customer's individual interests and preferences. Prior research in this area has focused on how to maximize knowledge mined from transaction logs to generate recommendations which are highly similar to the individual's past preferences. However, it remains an empirical question as to whether a recommendation closely matched with previous transactions is most likely to influence choice behavior? In this study, we postulate that a recommendation closely matched with previous transactions may not be the most efficient in biasing an..