Automatic document prior feature selection for web retrieval

Authors:
Jie Peng;Craig Macdonald;Iadh Ounis
Affiliations:
University of Glasgow, Glasgow, United Kngdm;University of Glasgow, Glasgow, United Kngdm;University of Glasgow, Glasgow, United Kngdm
Venue:
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Year:
2008

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Abstract

Document prior features, such as Pagerank and URL depth, can improve the retrieval effectiveness of Web Information Retrieval (IR) systems. However, not all queries equally benefit from the application of a document prior feature. This paper aims to investigate whether the retrieval performance can be further enhanced by selecting the best document prior feature on a per-query basis. We present a novel method for selecting the best document prior feature on a per-query basis. We evaluate our technique on the TREC .GOV Web test collection and its associated TREC 2003 Web search tasks. Our experiments demonstrate the effectiveness and robustness of our proposed selection method.