The DOI matches its Crossref record.
Novelty and redundancy detection in adaptive filtering
Abstract
This paper addresses the problem of extending an adaptive information filtering system to make decisions about the novelty and redundancy of relevant documents. It argues that relevance and redundance should each be modelled explicitly and separately. A set of five redundancy measures are proposed and evaluated in experiments with and without redundancy thresholds. The experimental results demonstrate that the cosine similarity metric and a redundancy measure based on a mixture of language models are both effective for identifying redundant documents.
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Cited by
Toward Scalable Systems for Big Data Analytics: A Technology Tutorial
Hu, H., Wen, Y., Chua, T.-S., & Li, X. (2014) · IEEE Access · doi:10.1109/access.2014.2332453 · Cited by 1,099
Kaminskas, M., & Bridge, D. (2017) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/2926720 · Cited by 465
SEA: Segment-enrich-annotate paradigm for adapting dialog-based content for improved accessibility
Candan, K. S., Dönderler, M. E., Hedgpeth, T., Kim, J. W., Li, Q., & Sapino, M. L. (2009) · ACM Transactions on Information Systems · doi:10.1145/1508850.1508853 · Cited by 8
Metadata
- Authors
- Zhang, Y., Callan, J., & Minka, T.
- Venue
- Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 2002-08-11
- Pages
- 81-88
- OpenAlex
- W1981825277
- Cited by
- 439
- References
- 0
- FWCI
- 8.93
- Type
- conference paper
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
