Some like it hot - visual guidance for preference prediction

For people first impressions of someone are of determining importance. They are hard to alter through further information. This begs the question if a computer can reach the same judgement. Earlier research has already pointed out that age, gender, and average attractiveness can be estimated with reasonable precision. We improve the state-of-the-art, but also predict - based on someone's known preferences - how much that particular person is attracted to a novel face. Our computational pipeline comprises a face detector, convolutional neural networks for the extraction of deep features, standard support vector regression for gender, age and facial beauty, and - as the main novelties - visual regularized collaborative filtering to infer inter-person preferences as well as a novel regression technique for handling visual queries without rating history. We validate the method using a very large dataset from a dating site as well as images from celebrities. Our experiments yield convincing results, i.e. we predict 76% of the ratings correctly solely based on an image, and reveal some sociologically relevant conclusions. We also validate our collaborative filtering solution on the standard MovieLens rating dataset, augmented with movie posters, to predict an individual's movie rating. We demonstrate our algorithms on howhot.io which went viral around the Internet with more than 50 million pictures evaluated in the first month.Comment: accepted for publication at CVPR 201

PERSONALIZED INDEXING OF MUSIC BY EMOTIONS

How a person interprets music and what prompts a person to feel certain emotions are two very subjective things. This dissertation presents a method where a system can learn and track a user’s listening habits with the purpose of recommending songs that fit the user’s specific way of interpreting music and emotions. First a literature review is presented which shows an overview of the current state of recommender systems, as well as describing classifiers; then the process of collecting user data is discussed; then the process of training and testing personalized classifiers is described; finally a system combining the personalized classifiers with clustered data into a hierarchy of recommender systems is presented