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Local Metric Learning for Tag Recommendations in Social Networks

Boris Chidlovskii, Aymen Benzarti
We address the problem of tag recommendation for media objects, like images, videos, etc in social media sharing systems. We pro pose a framework that I) extracts both object features and the social context and 2) uses them to learn recommendation rules. The so cial 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 first used to guide the k-nearest neighbour method for the tag recommendation. We then enhance the method 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 multitag objects and adopt it to the tag recommendation problems.
International ACM Conference on DocEng,Mountain View, California, USA, September 19-22 September, 2011.