Dorota Glowacka
Empirical Parameterization of Exploratory Search Systems Based on Bandit Algorithms
Abstract: 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. In this talk, we discuss two approaches how to optimise exploratory search systems based on bandit algorithms. First, 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. Our second approach aims to dynamically adapt exploration and exploitation in a manner commensurate with the user's individual requirements for each search session. We present a novel study design together with a regression model for predicting the optimal exploration rate based on simple metrics from the first iteration, such as clicks and reading time. We perform model selection based on the data collected from a user study and show that predictions are consistent with user feedback.