Feature guided automated collaborative filtering
Yezdezard Lashkari · DSpace@MIT (Massachusetts Institute of Technology) · 1995
Information filtering systems have traditionally relied on some form of content analysis of documents to represent a profile of user interests. Such content filtering is generally ineffective in domains with diverse media types such as audio, video, and images, because machineanalysis of such media is hard. Recently, information filtering systems relying primarily on human evaluations of documents have been built. Such automated collaborative filtering systems work by discovering correlations in evaluations of documents amongst users, and by using these correlations to recommend new documents. However, such systems rely on the implicit assumption that all the features of a document are equally important to a user's evaluation of that document. This assumption breaks down in broad domains, (such as all documents in the World Wide Web), where users correlate well only for some features of a document they evaluated similarly. This thesis claims that using a combination of easily extractib...