User-aware page classification in a search engine
Rachel Aires, Sandra Maria Aluísio, Diana Santos · 2005
In this paper we investigate the hypothesis that classification of Web pages according to the general user intentions is feasible and useful. As a preliminary study we look into the use of 46 linguistic features to classify texts according to genres and text types; we then employ the same features to train a classifier that decides which possible user need(s) a Web page may satisfy. We also report on experiments for customizing searching systems with the same set of features to train a classifier that helps users discriminate among their specific needs. Finally, we describe some user input that makes us confident on the utility of the approach.