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An SVM Based Approach to Feature Selection for Topical Relevance Judgement of Feeds

Relevance judgement method is an essential part of a feed search engine, which determines candidates for ranking query results. However, the different characteristics of feeds from traditional web pages may make existing relevance judgement approaches proposed for web page retrieval produce unsatisfactory result. Compared to web pages, feed is a structured document in that it contains several data elements, including title and description. In addition, feed is a temporal document since it dynamically publishes information on some specific topics over time. Accordingly, the relevance judgement 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. We conducted extensive experiments to investigate the problem using support vector machine on real-world data set, and found the significant features that can be used for feed search services.