Nikos Sarkas
Abstract
User generated content has been fueling an explosion in the amount of available textual data. In this context, it is also common for users to express, either explicitly (through numerical ratings) or implicitly, their views and opinions on products, events, etc. This wealth of textual information necessitates the development of novel searching and data exploration paradigms.
In this paper we propose a new searching model, similar in spirit to faceted search, that enables the progressive refinement of a keyword-query result. However, in contrast to faceted search which utilizes domain-specific and hard-to-extract document attributes, the refinement process is driven by suggesting interesting expansions of the original query with additional search terms. Our query-driven and domain-neutral approach employs surprising word co-occurrence patterns and (optionally) numerical user ratings in order to identify meaningful top-k query expansions and allow one to focus on a particularly interesting subset of the original result set.
The proposed functionality is supported by a framework that is computationally efficient and nimble in terms of storage requirements. Our solution is grounded on Convex Optimization principles that allow us to exploit the pruning opportunities offered by the natural top-k formulation of our problem. The performance benefits offered by our solution are verified using both synthetic data and large real data sets comprised of blog posts.
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Index Terms
- Measure-driven keyword-query expansion
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