A Proposal for User-Focused Evaluation and Prediction of Information Seeking Process

One of the ways IR systems help searchers is by predicting or assuming what could be useful for their information needs based on analyzing information objects (documents, queries) and finding other related objects that may be relevant. Such approaches often ignore the underlying search process of information seeking, thus forgoing opportunities for making process-based recommendations. To overcome this limitation, we are proposing a new approach that analyzes a searcher’s current processes to forecast his likelihood of achieving a certain level of success in the future. Specifically, we propose a machine-learning based method to dynamically evaluate and predict search performance several time-steps ahead at each given time point of the search process during an exploratory search task. Our prediction method uses a collection of features extracted solely from the search process such as dwell time, query entropy and relevance judgment in order to evaluate whether it will lead to low or high performance in the future. Experiments that simulate the effects of switching search paths show a significant number of subpar search processes improving after the recommended switch. In effect, the work reported here provides a new framework for evaluating search processes and predicting search performance. Importantly, this approach is based on user processes, and independent of any IR system allowing for wider applicability that ranges from searching to recommendations.