Local Metric Learning for Tag Recommendation in Social Networks

We address the problem of tag recommendation for media objects, like images and videos, in social media sharing systems. We propose a framework that 1) extracts both object features and the social context and 2) uses them to learn recommendation rules. The social context is described by different types of information, such as a user's personal objects, the objects of a user's social contacts, the importance of the user in the social network, etc. Both object features and the social context are used to guide the k-nearest neighbour method for the tag recommendation. We then enhance the kNN by the local topology adjustment on how the nearest neighbours are selected. We learn a local transformation of the feature space surrounding a given object which pushes together objects with the same tags and puts apart objects with different tags. We show how to learn the Mahalanobis distance metric on multi-tag objects and adopt it to the tag recommendation problem.