Name
Abstract | ||
|---|---|---|
Modeling the response time of search engines is an important task for many applications such as resource selection in federated text search. Limited research has been conducted to address this task. Prior research calculated the search response time of all queries in the same way either with the average response time of several sample queries or with a single probability distribution, which is irrelevant to the characteristics of queries. However, the search response time may vary a lot for different types of queries. This paper proposes a novel query-specific and source-specific approach to model search response time. Some training data is acquired by measuring the search response time of some sample queries from a search engine. Then, a query-specific model is estimated with the training data and their corresponding response times by utilizing Ridge Regression. The obtained model can be used to predict search response times for new queries. A set of empirical studies are conducted to show the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1571941.1572100 | SIGIR |
Keywords | Field | DocType |
query-specific model,training data,response time,sample query,model search response time,corresponding response time,search response time,federated text search,search engine,source selection,average response time,ridge regression,probability distribution,empirical study | Data mining,Computer science,Response time,Probability distribution,Artificial intelligence,Empirical research,Training set,Search engine,Regression,Information retrieval,Full text search,Beam search,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
2 | ||