Name
Abstract | ||
|---|---|---|
Different queries require different ranking methods. It is however challenging to determine what queries are similar, and how to rank documents for them. In this paper, we propose a new method to cluster queries according to the similarity determined based on URLs in their answers. We then train specific ranking models for each query cluster. In addition, a cluster-specific measure of authority is defined to favor documents from authoritative websites on the corresponding topics. The proposed approach is tested using data from a search engine. It turns out that our proposed topic-dependent models can significantly improve the search results of eight most popular categories of queries. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1645953.1646174 | CIKM |
Keywords | Field | DocType |
query cluster,different ranking method,clustering query,specific ranking model,search engine,proposed topic-dependent model,different query,authoritative web,cluster-specific measure,better document ranking,search result,clustering | Data mining,Search engine,Ranking SVM,Information retrieval,Ranking,Computer science,Ranking (information retrieval),Cluster analysis | Conference |
Citations | PageRank | References |
2 | 0.38 | 13 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Liu | 1 | 9 | 1.22 |
| Liang-Jie Zhang | 2 | 982 | 138.17 |
| Ruihua Song | 3 | 1138 | 59.33 |
| Jian-yun Nie | 4 | 3681 | 238.61 |
| Ji-Rong Wen | 5 | 4431 | 265.98 |