Name
Abstract | ||
|---|---|---|
Learning to rank is one of the most hot research areas in information retrieval, among which listwise approach is an important research direction and the methods that directly optimizing evaluation metrics in listwise approach have been used for optimizing some important ranking evaluation metrics, such as MAP, NDCG and etc. In this paper, the structural SVMs method is employed to optimize the Expected Reciprocal Rank(ERR) criterion which is named SVMERR for short. It is compared with state-of-the-art algorithms. Experimental results show that SVMERR outperforms other methods on OHSUMED dataset and TD2003 dataset, which also indicate that optimizing ERR criterion could improve the ranking performance. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-25631-8_9 | AIRS |
Keywords | Field | DocType |
reciprocal rank,td2003 dataset,ranking performance,ohsumed dataset,optimizing err criterion,expected reciprocal rank,hot research area,listwise approach,important research direction,optimizing evaluation metrics,important ranking evaluation metrics,information retrieval,learning to rank | Learning to rank,Data mining,Reciprocal,Ranking,Computer science,Support vector machine,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | Citations |
7097 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ping Zhang | 1 | 1 | 0.68 |
| Hongfei Lin | 2 | 768 | 122.52 |
| Yuan Lin | 3 | 104 | 16.38 |
| Jiajin Wu | 4 | 16 | 3.93 |