@inproceedings{raiber2015learning,
author = {Raiber, Fiana and Kurland, Oren and Radlinski, Filip and Shokouhi, Milad},
title = {Learning Asymmetric Co-Relevance},
booktitle = {Proceedings of the ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR)},
year = {2015},
month = {September},
abstract = {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.},
publisher = {ACM - Association for Computing Machinery},
url = {http://approjects.co.za/?big=en-us/research/publication/learning-asymmetric-co-relevance/},
edition = {Proceedings of the ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR)},
}