Using Context-sensitive Statistics to Rank Documents

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 significantly on the context. The underlying reason is that the underlying keyword statistics differ significantly. The query evaluation challenge is the computation of keyword statistics at run time, which involves expensive online aggregations. We appropriately leverage and extend materialized view research in order to deliver algorithms and data structures that evaluate contextsensitive 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 context-sensitive ranking generally improves the ranking quality, while our materialized view-based technique improves the query efficiency.