Affiliations: Department of Industrial Engineering, Seoul National University, Seoul, Korea
Correspondence:
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Corresponding author: Jonghun Park, Department of Industrial Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 151-744, Korea. Tel.: +82 2 880 7174; Fax: +82 2 889 8560; E-mail: [email protected].
Note: [1] A preliminary version of this paper was presented at the SIGIR 2010 Workshop on Feature Generation and Selection for Information Retrieval.
Abstract: Feed has become a popular way to effectively distribute and acquire information on the web. The explosive growth of feeds demands a search engine that can help users quickly discover feeds of their interests. Retrieval effectiveness of feed search engine highly depends on a relevance assessment method that determines candidates for ranking query results. However, existing relevance assessment approaches proposed for web page retrieval may produce unsatisfactory result due to the different characteristics of feeds from traditional web pages. Compared to web pages, feed is a dynamic document since it continually generates information on some specific topics. In addition, it is a structured document that consists of several data elements such as title and description. Accordingly, the relevance assessment method for feed retrieval needs to effectively address these unique characteristics of feeds. This paper considers a problem of identifying significant features which are a feature set created from feed data elements, with the aim of improving effectiveness of feed retrieval while at the same time reducing computational cost. We conducted extensive experiments to investigate the problem using support vector machine on real-world data sets, and found the significant features that can be employed for feed search services.
Keywords: Feed search, feed, feature identification, relevance assessment, text classification, search engine, information retrieval
