This item is a Poster.
- Wu, Hao - Zhejiang University
- Qiu, Guang - Zhejiang University
- He, Xiaofei - Zhejiang University
- Shi, Yuan - Zhejiang University
- Qu, Mingcheng - Zhejiang University
- Shen, Jing - China Disabled Persons' Federation Information Center
- Bu, Jiajun - Zhejiang University
- Chen, Chun - Zhejiang University
Published Version
![[img]](../style/images/fileicons/application_pdf.png)
| PDF (539Kb) |
Abstract
This paper proposes an efficient relevance feedback based interactive model for keyword generation in sponsored search advertising. We formulate the ranking of relevant terms as a supervised learning problem and suggest new terms for the seed by leveraging user relevance feedback information. Active learning is employed to select the most informative samples from a set of candidate terms for user labeling. Experiments show our approach improves the relevance of generated terms significantly with little user effort required.
Export Record As...
- HTML Citation
- ASCII Citation
- Resource Map
- OpenURL ContextObject
- EndNote
- BibTeX
- OpenURL ContextObject in Span
- MODS
- DIDL
- EP3 XML
- JSON
- Dublin Core
- Reference Manager
- Eprints Application Profile
- Simple Metadata
- Refer
- METS
