Name
Abstract | ||
|---|---|---|
A number of existing information retrieval systems propose the notion of query context to combine the knowledge of query and user into retrieval to reveal the most exact description of user's information needs. In this paper we interpret query context as a document consisting of sentences related to the current query. This kind of query context is used to re-estimate the relevance probabilities of top-ranked documents and then re-rank top-ranked documents. The experiments show that the proposed context-based approach for information retrieval can greatly improved relevance of search results. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1145/1242572.1242743 | WWW |
Keywords | Field | DocType |
re-rank top-ranked document,information retrieval system,top-ranked document,information retrieval,information need,current query,exact description,improved relevance,relevance probability,query context | Query optimization,Data mining,Web search query,Query language,Query expansion,Information retrieval,Computer science,Web query classification,Ranking (information retrieval),Relevance (information retrieval),Concept search | Conference |
Citations | PageRank | References |
1 | 0.35 | 7 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Keke Cai | 1 | 243 | 15.36 |
| Chun Chen | 2 | 4727 | 246.28 |
| Jiajun Bu | 3 | 4106 | 211.52 |
| Peng Huang | 4 | 96 | 4.92 |
| Zhiming Kang | 5 | 12 | 1.24 |