Name
Abstract | ||
|---|---|---|
With the explosive emergence of vertical search domains, applying the broad-based ranking model directly to different domains is no longer desirable due to domain differences, while building a unique ranking model for each domain is both laborious for labeling data and time consuming for training models. In this paper, we address these difficulties by proposing a regularization-based algorithm called ranking adaptation SVM (RA-SVM), through which we can adapt an existing ranking model to a new domain, so that the amount of labeled data and the training cost is reduced while the performance is still guaranteed. Our algorithm only requires the prediction from the existing ranking models, rather than their internal representations or the data from auxiliary domains. In addition, we assume that documents similar in the domain-specific feature space should have consistent rankings, and add some constraints to control the margin and slack variables of RA-SVM adaptively. Finally, ranking adaptability measurement is proposed to quantitatively estimate if an existing ranking model can be adapted to a new domain. Experiments performed over Letor and two large scale data sets crawled from a commercial search engine demonstrate the applicabilities of the proposed ranking adaptation algorithms and the ranking adaptability measurement. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/TKDE.2010.252 | Knowledge and Data Engineering, IEEE Transactions |
Keywords | Field | DocType |
new domain,ranking model adaptation,ranking adaptation,domain-specific search,auxiliary domain,existing ranking model,unique ranking model,proposed ranking adaptation algorithm,broad-based ranking model,different domain,consistent ranking,ranking adaptability measurement,search engine,support vector machine,data model,predictive models,data handling,data models,search engines,support vector machines,learning to rank,prediction algorithms,feature space,prediction model,information retrieval | Vertical search,Learning to rank,Data modeling,Data mining,Slack variable,Ranking SVM,Ranking,Computer science,Support vector machine,Ranking (information retrieval),Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
24 | 4 | 1041-4347 |
Citations | PageRank | References |
45 | 1.46 | 28 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bo Geng | 1 | 604 | 22.44 |
| Linjun Yang | 2 | 1556 | 65.20 |
| Chao Xu | 3 | 1327 | 62.65 |
| Xian-Sheng Hua | 4 | 6566 | 328.17 |