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Context-sensitive Ranking for Document Retrieval

Summary: Context-sensitive ranking treats a context as a user-specified sub-collection, so keyword statistics and rankings vary across contexts. It extends materialized-view techniques to precompute views for large contexts and use them at query time to compute statistics and ranking scores. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4429
Venue
SIGMOD
Year
2011
Pagerank
4.3556432e-05
Overall Rank
9,335 | 35.06%
DOI
-

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
10,090 Integrating Vector Databases across Embedding Models 2026 SIGMOD 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 8 of 8 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
11 Implementing Data Cubes Efficiently 1996 SIGMOD 0.0011708144
273 Approximate Computation of Multidimensional Aggregates of Sparse Data Using Wavelets 1999 SIGMOD 0.00029390945
681 Materialized View Selection in a Multidimensional Database 1997 VLDB 0.00018203591
1,021 Materialized View Selection for Multidimensional Datasets* 1998 VLDB 0.00014619259
1,359 Range Queries in OLAP Data Cubes 1997 SIGMOD 0.0001238588
3,583 A Formal Perspective on the View Selection Problem 2001 VLDB 6.9463532e-05
4,444 Hierarchical Cubes for Range-Sum Queries 1999 VLDB 6.1831691e-05
4,637 Context-Sensitive Ranking 2006 SIGMOD 6.0303293e-05
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