Context-sensitive ranking for document retrieval

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Context-sensitive ranking for document retrieval
We study the problem of context-sensitive ranking for document retrieval, where a context is defined as a sub-collection of documents, and is specified by queries provided by domain-interested users. The motivation of context-sensitive search is that the ranking of the same keyword query generally depends on the context. The reason is that the underlying keyword statistics differ significantly from one context to another. The query evaluation challenge is the computation of keyword statistics at runtime, which involves expensive online aggregations. We appropriately leverage and extend materialized view research in order to deliver algorithms and data structures that evaluate context-sensitive queries efficiently. Specifically, a number of views are selected and materialized, each corresponding to one or more large contexts. Materialized views are used at query time to compute statistics which are used to compute ranking scores. Experimental results show that the contextsensitive ...
Liang Jeff Chen, Yannis Papakonstantinou
Added 17 Sep 2011
Updated 17 Sep 2011
Type Journal
Year 2011
Authors Liang Jeff Chen, Yannis Papakonstantinou
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