Y!Q

Abstract

Contextual search tries to better capture a user's information need by augmenting the user's query with contextual information extracted from the search context (for example, terms from the web page the user is currently reading or a file the user is currently editing).This paper presents Y!Q---a first of its kind large-scale contextual search system---and provides an overview of its system design and architecture. Y!Q solves two major problems. First, how to capture high quality search context. Second, how to use that context in a way to improve the relevancy of search queries. To address the first problem, Y!Q introduces an information widget that captures precise search context and provides convenient access to its functionality at the point of inspiration. For example, Y!Q can be easily embedded into web pages using a web API, or it can be integrated into a web browser toolbar. This paper provides an overview of Y!Q's user interaction design, highlighting its novel aspects for capturing high quality search context.To address the second problem, Y!Q uses a semantic network for analyzing search context, possibly resolving ambiguous terms, and generating a contextual digest comprising its key concepts. This digest is passed through a query planner and rewriting framework for augmenting a user's search query with relevant context terms to improve the overall search relevancy and experience. We show experimental results comparing contextual Y!Q search results side-by-side with regular Yahoo! web search results. This evaluation suggests that Y!Q results are considered significantly more relevant.The paper also identifies interesting research problems and argues that contextual search may represent the next major step in the evolution of web search engines.