Searching with Context
Contextual search refers to proactively capturing the information need of a user by automatically augmenting the user query with information extracted from the search context; for example, by using terms from the web page the user is currently browsing or a file the user is currently editing.
We present three different algorithms to implement contextual search for the Web. The first, query rewriting (QR), augments each query with appropriate terms from the search context and uses an off-the-shelf web search engine to answer this augmented query. The second, rank-biasing (RB), generates a representation of the context and answers queries using a custom-built search engine that exploits this representation. The third, iterative filtering meta-search (IFM), generates multiple subqueries based on the user query and appropriate terms from the search context, uses an off-the-shelf search engine to answer these subqueries, and re-ranks the results of the subqueries using rank aggregation methods.
We extensively evaluate the three methods using 200 contexts and over 24,000 human relevance judgments of search results. We show that while QR works surprisingly well, the relevance and recall can be improved using RB and substantially more using IFM. Thus, QR, RB, and IFM represent a cost-effective design spectrum for contextual search.
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