article · 2011
Selection of Multimedia Data Source Based on User Feedback
Song Deng, Changxuan Wan, Xiping Liu
Cited by 0Abstract and details only
Abstract
There are hundreds or thousands of web data sources providing data of relevance to a particular domain on the Web, so how to find a suitable set of sources quickly to integrate from a number of sources is becoming more and more important. Traditional data sources selection methods that are based on data quality do not consider quality characteristics of data sources under the different domains, but select the same quality dimensions for quality evaluation models. As a result, accuracies of a traditional method under different domains are quite different. In light of this, we propose a Web multimedia source selection method based on user feedback. We gain accurate core quality dimensions of data sources under the multimedia domain based on user feedback, and then achieve a quality estimation model of the multimedia data sources. The experiments on hundreds of real-world multimedia data sources have proved that our approach is efficient and accurate.
Cite this paper
Deng, S., Wan, C., & Liu, X. (2011). Selection of Multimedia Data Source Based on User Feedback. https://doi.org/10.1109/icmecg.2011.21
Deng, Song, et al. “Selection of Multimedia Data Source Based on User Feedback.” vol. 25, 2011, pp. 285–89, https://doi.org/10.1109/icmecg.2011.21.
Deng, Song, Changxuan Wan, and Xiping Liu. 2011. “Selection of Multimedia Data Source Based on User Feedback.” https://doi.org/10.1109/icmecg.2011.21.
Deng, S., Wan, C. and Liu, X. (2011) ‘Selection of Multimedia Data Source Based on User Feedback’. Available at: https://doi.org/10.1109/icmecg.2011.21.
@article{deng2011selection,
title = {{Selection of Multimedia Data Source Based on User Feedback}},
author = {Deng, Song and Wan, Changxuan and Liu, Xiping},
volume = {25},
pages = {285--289},
year = {2011},
doi = {10.1109/icmecg.2011.21}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.