Learning Asymmetric Co-Relevance
- Fiana Raiber ,
- Oren Kurland ,
- Filip Radlinski ,
- Milad Shokouhi
Proceedings of the ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR) |
Published by ACM - Association for Computing Machinery
Several applications in information retrieval rely on asymmetric co-relevance estimation; that is, estimating the relevance of a document to a query under the assumption that another document is relevant. We present a supervised model for learning an asymmetric co-relevance estimate. The model uses different types of similarities with the assumed relevant document and the query, as well as document-quality measures. Empirical evaluation demonstrates the merits of using the co-relevance estimate in various applications, including cluster-based and graph-based document retrieval. Specifically, the resultant performance transcends that of using a wide variety of alternative estimates, mostly symmetric inter-document similarity measures that dominate past work.
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