Name
Abstract | ||
|---|---|---|
In this paper, we propose to model the blended search problem by assuming conditional dependencies among queries, VSEs and search results. The probability distributions of this model are learned from search engine query log through unigram language model. Our experimental exploration shows that, (1) a large number of queries in generic Web search have vertical search intentions; and (2) our proposed algorithm can effectively blend vertical search results into generic Web search, which can improve the Mean Average Precision (MAP) by as much as 16% compared to traditional Web search without blending. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1526709.1526863 | WWW |
Keywords | Field | DocType |
probabilistic model,vertical search intention,traditional web search,generic web search,blended search problem,vertical search result,conditional dependency,mean average precision,search engine query log,unigram language model,search result,language model,probability distribution,search engine | Data mining,Vertical search,Search algorithm,Computer science,Jump search,Artificial intelligence,Iterative deepening depth-first search,Web search query,World Wide Web,Interpolation search,Beam search,Best-first search,Machine learning | Conference |
Citations | PageRank | References |
3 | 0.38 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ning Liu | 1 | 253 | 15.62 |
| Jun Yan | 2 | 1798 | 85.25 |
| Zheng Chen | 3 | 5019 | 256.89 |