@inproceedings{citeulike:1542210,
title = {A regression framework for learning ranking functions using relative relevance judgments},
address = {New York, NY, USA},
author = {Zhaohui Zheng and Keke Chen and Gordon Sun and Hongyuan Zha},
booktitle = {SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval},
pages = {287--294},
publisher = {ACM Press},
url = {http://portal.acm.org/citation.cfm?id=1277741.1277792},
year = {2007},
posted-at = {2007-10-24 13:20:47}, citeulike-article-id = {1542210}, priority = {2}, isbn = {9781595935977}, comment = {Distinquish between absolut and relative relevance judgements whether the document is relevant at all and whether one particular document is more relevant to a query than another one, resp.
Problem stmt:
Given feature vectors for two query-document pairs x and y. A relative judgement indicates that e.g. x>y - x should be ranked higher than y.
The features of the examples can be partitioned into three categories:
Query feature: Query length, language,...
Document feature: PR,inlinks,amount of anchor text,...
Query-Document features: term co-ocurrence, cosine-sim,...}, doi = {10.1145/1277741.1277792},
keywords = {feedback, information, judgements, learning, machine, ranking, relevance, retrieval }
}
::