Automatic Query Refining Based on Eye-Tracking Feedback

Abstract

This paper presents a new method named AQueReBET, which automatically refines a query set by an information seeker searching on the web. A revelation of the intention of an information seeker who is running a search can bring a significant improvement to the search process, and to browsing as well. It is practically impossible to acquire such intention by the explicit indication (feedback) due to the fact that web browsing takes place in real time. Therefore the intention must be determined in some other way. We hypothesize that it can be approximated by means of the implicit feedback preferably in the form of data from an eye tracker and mouse. We propose a method which automatically refines a seeker’s search query and thus we can offer documents with higher relevance, decrease the number of query reformulations and increase the seeker’s satisfaction. The query refinement is based on an analysis of gaze data from an eye tracker and also on groupization. In the proposed method, we calculate word-level importance based on term frequency, term uniqueness (tf-idf) and total fixation duration within the subdocument (word's snippet in search results).