New machine learning methods for collaborative filtering, document retrieval and image retrieval

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The research will use novel machine learning algorithms recently developed at the University of Toronto toimprove several aspects of web-based systems.Collaborative filtering systems recommend products such as books or DVD's to a user based on similaritiesbetween that user and other users whose preferences are known. There are many different ways of using thesesimilarities and new, non-linear machine learning methods are already more accurate than the methodscurrently in use. These methods will be developed further.A newly discovered method called "semantic hashing" is much faster than existing methods at retrievingdocuments that are similar to a query document. Semantic hashing uses machine learning to associate anaddress with each document in the database in such a way that similar documents have addresses that differ byonly a few bits. Given the address of the query document, similar documents can than be found very rapidly bysimply looking at neighboring addresses. The research will improve the ability of the machine learningalgorithms to assign similar addresses to similar documents. It will also apply similar methods to imageretrieval. This will involve learning features of images that are as informative about the content of an image aswords are about the content of a document.New methods will be developed for learning features from large databases of unlabeled satellite images, so thatclassifiers for types of ground cover can be learned with fewer labeled training examples. The classifiers willalso be improved by learning to use the contextual information provided by the labels of nearby regions.