Name
Abstract | ||
|---|---|---|
We investigate the idea of finding semantically related search engine queries based on their temporal correlation; in other words, we infer that two queries are related if their popularities behave similarly over time. To this end, we first define a new measure of the temporal correlation of two queries based on the correlation coefficient of their frequency functions. We then conduct extensive experiments using our measure on two massive query streams from the MSN search engine, revealing that this technique can discover a wide range of semantically similar queries. Finally, we develop a method of efficiently finding the highest correlated queries for a given input query using far less space and time than the naive approach, making real-time implementation possible. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1145/1060745.1060752 | WWW |
Keywords | Field | DocType |
semantic similarity,new measure,massive query stream,semantically similar query,msn search engine,input query,highest correlated query,semantically related search engine,temporal correlation,extensive experiment,correlation coefficient,search engines,search engine | Semantic similarity,Correlation coefficient,Data mining,Metasearch engine,Search engine,Information retrieval,Semantic search,Computer science,Correlation | Conference |
ISBN | Citations | PageRank |
1-59593-046-9 | 117 | 8.79 |
References | Authors | |
10 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Steve Chien | 1 | 323 | 19.12 |
| Nicole Immorlica | 2 | 1636 | 100.87 |