Name
Abstract | ||
|---|---|---|
We consider the problem of estimating the size of a collection of documents using only a standard query interface. Our main idea is to construct an unbiased and low-variance estimator that can closely approximate the size of any set of documents defined by certain conditions, including that each document in the set must match at least one query from a uniformly sampleable query pool of known size, fixed in advance.Using this basic estimator, we propose two approaches to estimating corpus size. The first approach requires a uniform random sample of documents from the corpus. The second approach avoids this notoriously difficult sample generation problem, and instead uses two fairly uncorrelated sets of terms as query pools; the accuracy of the second approach depends on the degree of correlation among the two sets of terms.Experiments on a large TREC collection and on three major search engines demonstrates the effectiveness of our algorithms. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1145/1183614.1183699 | CIKM |
Keywords | Field | DocType |
estimator,random sampling,search engine | Data mining,Search engine,Information retrieval,Computer science,Uncorrelated,Ranking (information retrieval),Correlation,Sampling (statistics),Estimator | Conference |
ISBN | Citations | PageRank |
1-59593-433-2 | 35 | 1.96 |
References | Authors | |
14 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrei Broder | 1 | 7357 | 920.20 |
| Marcus Fontoura | 2 | 1116 | 61.74 |
| Vanja Josifovski | 3 | 2265 | 148.84 |
| Ravi Kumar | 4 | 13932 | 1642.48 |
| R Motwani | 5 | 19085 | 1986.61 |
| Shubha U. Nabar | 6 | 382 | 20.79 |
| Rina Panigrahy | 7 | 3203 | 269.05 |
| Andrew Tomkins | 8 | 9388 | 1401.23 |
| Ying Xu | 9 | 242 | 16.10 |