The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A framework to predict the quality of answers with non-textual features
Open accessAbstract
New types of document collections are being developed by various web services. The service providers keep track of non-textual features such as click counts. In this paper, we present a framework to use non-textual features to predict the quality of documents. We also show our quality measure can be successfully incorporated into the language modeling-based retrieval model. We test our approach on a collection of question and answer pairs gathered from a community based question answering service where people ask and answer questions. Experimental results using our quality measure show a significant improvement over our baseline.
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Cites
The anatomy of a large-scale hypertextual Web search engine
Brin, S., & Page, L. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00110-x · Cited by 16,004
Strong, D. M., Lee, Y. W., & Wang, R. Y. (1997) · Communications of the ACM · doi:10.1145/253769.253804 · Cited by 1,298
Cited by
Reputation Transferability in Online Labor Markets
Kokkodis, M., & Ipeirotis, P. G. (2016) · Management Science · doi:10.1287/mnsc.2015.2217 · Cited by 166
Metadata
- Authors
- Jeon, J., Croft, W. B., Lee, J. H., & Park, S.
- Venue
- Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 2006-08-06
- Pages
- 228-235
- OpenAlex
- W2102956348
- Cited by
- 348
- References
- 2
- FWCI
- 10.52
- Type
- conference paper
- Language
- en
- Open access
- green
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- The paper
- Jeon, J., Croft, W. B., Lee, J. H., & Park, S. (2006). A framework to predict the quality of answers with non-textual features. Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval. https://doi.org/10.1145/1148170.1148212
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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