Leveraging exploratory search with personality traits and interactional context

Exploratory search is a type of information seeking used by searchers who are either unfamiliar with the domain of their goal, are unsure about the ways to achieve their goals or uncertain about their goals in the first place. We present a method that utilizes interactional context and personality information in order to proactively prompt users to undertake actions for improving exploratory search and its outcome. Our approach is based on inferring exploration patterns based on the logged past behavior of users in order to produce models of behavior, which in turn are used to predict the next action in the current context. The user is classified into specific groups of users that share personality traits for which we have analyzed their search behaviors. At the same time, we assume that the users who belong within the same group show similar exploration tactics to reach their goal such as the sequence of actions performed. Having the models, we show how we can predict the next interaction of the user given a specific sequence of actions of the current session. In this way, we assist users in their exploration process and act proactively by providing meaningful recommendations and prompts towards possibly undiscovered facets of the topic under investigation.