Improving Mining Facets for Queries from their User Search Results

Abstract

Dynamic Faceted Search Systems have gained prominence in research as one of the exploratory search approaches that support complex search tasks. They provide facets to users about the information space and allow them to refine their dynamic search query and navigate back and forth between resources on a single results page. When the information available in the collection being searched across increases, so does the number of associated facets. This can make it impractical to display all of the facets at once. To tackle this problem, Dynamic Faceted Search employs methods for facet ranking. Ranking methods can be based on the information structure, the textual queries issued by the user, or the usage logs. Such methods reflect neither the importance of the facets nor the user interests. I focus on the problem of ranking facets from knowledge bases and Linked

Data. Knowledge bases have the advantage of containing high quality structured data. With the increasing size and complexity of Linked datasets, the task of deciding which facets should be manifest to the user, and in which order, becomes more difficult. Moreover, the idea of personalizing exploratory search can be challenging and tricky, since personalization in IR (specifically precision-oriented search engines) implicitly implies narrowing and focusing the information space to retrieve the most relevant results according to the users' interests and desires. On the contrary, exploratory search systems are typically recall-oriented and they favor covering as much from the information space as possible. They also encourage diversifying the user knowledge to help them learn and discover the unknown. The generation of a ranked list of facets should be a dynamic process for a number of reasons. First of all, manually setting up facets is a time consuming task which relies upon domain experts. Second, it is not practical on large, multi-domain datasets. Even one-off automatic facet generation and ranking might not be suitable for data that changes and grows over time. Lastly, the relevance of facets can be user, query and context dependant. I am proposing a personalized approach to the dynamic ranking of facets. The approach combines different sources of information to recommend the most relevant facets.