Balancing Exploration and Exploitation: Empirical Parameterization of Exploratory Search Systems

Exploratory searches are where a user has insufficient knowledge to define exact search criteria or does not otherwise know what they are looking for. Reinforcement learning techniques have demonstrated great potential for supporting exploratory search in information retrieval systems as they allow the system to trade-off exploration (presenting the user with alternatives topics) and exploitation (moving toward more specific topics). Users of such systems, however, often feel that the system is not responsive to user needs. This problem is not an inherent feature of such systems, but is caused by the exploration rate parameter being inappropriately tuned for a given system, dataset or user.

We present a user study to analyze how different exploration rates affect search performance, user satisfaction, and the number of documents selected. We show that the tradeoff between exploration and exploitation can be modelled as a direct relationship between the exploration rate parameter from the reinforcement learning algorithm and the number of relevant documents returned to the user over the course of a search session. We define the optimal exploration/exploitation trade-off as where this relationship is maximised and show this point to be broadly concordant with user satisfaction and performance.