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Inference of online social network users' attributes and interests has beenan active research topic. Accurate identification of users' attributes andinterests is crucial for improving the performance of personalization andrecommender systems. Most of the existing works have focused on textual contentgenerated by the users and have successfully used it for predicting users'interests and other identifying attributes. However, little attention has beenpaid to user generated visual content (images) that is becoming increasinglypopular and pervasive in recent times. We posit that images posted by users ononline social networks are a reflection of topics they are interested in andpropose an approach to infer user attributes from images posted by them. Weanalyze the content of individual images and then aggregate the image-levelknowledge to infer user-level interest distribution. We employ image-levelsimilarity to propagate the label information between images, as well asutilize the image category information derived from the user createdorganization structure to further propagate the category-level knowledge forall images. A real life social network dataset created from Pinterest is usedfor evaluation and the experimental results demonstrate the effectiveness ofour proposed approach.

A Picture Tells a Thousand Words -- About You! User Interest Profiling from User Generated Visual Content