using query context models to construct topical search engines
Today, if a website owner or blogger wants to provide a search interface on their web site, they have essentially two options: web search or site search. Site search is often too narrow and web search often too broad. We propose a context-specific alternative: the use of 'topical search engines' (TopS) providing results focused on a specific topic determined by the site owner. For example a photography blog could offer a search interface focused on photography. In this paper, we describe a promising new approach to easily create such topical search engines with minimal manual effort. In our approach, whenever we have enough contextual information, we alter ambiguous topic related queries issued to a generic search engine by adding contextual keywords derived from (topic-specific) query logs; the altered queries help focus the search engine's results to the specific topic of interest. Our solution is deployed as a query wrapper, requiring no change in the underlying search engine. We present techniques to automatically extract queries related to a topic from a web click graph, identify suitable query contexts from these topical queries, and use these contexts to alter queries that are ambiguous or under-specified. We present statistics on three topical search engine prototypes we created. We then describe an evaluation study with the prototypes we developed in the areas of photography and automobiles. We conducted three tests comparing these prototypes to baseline engines with and without fixed query refinements. In each test, we obtained preference judgments from over a hundred participants. Users showed a strong preference for TopS prototypes in all three tests, with statistically significant preference differences ranging from 16\% to 42\%.
query context click graph lexical generality subwebs topic focused search