Learning to annotate via social interaction analytics

Recent years have witnessed increased interests in exploiting automatic annotating techniques for managing and retrieving media contents. Previous studies on automatic annotating usually rely on the metadata which are often unavailable for use. Instead, multimedia contents usually arouse frequent preference-sensitive interactions in the online social networks of public social media platforms, which can be organized in the form of interaction graph for intensive study. Inspired by this observation, we propose a novel media annotating method based on the analytics of streaming social interactions of media content instead of the metadata. The basic assumption of our approach is that different types of social media content may attract latent social group with different preferences, thus generate different preference-sensitive interactions, which could be reflected as localized dense subgraph with clear preferences. To this end, we first iteratively select nodes from streaming records to build the preference-sensitive subgraphs, then uniformly extract several static and topologic features to describe these subgraphs, and finally integrate these features into a learning-to-rank framework for automatic annotating. Extensive experiments on several real-world date sets clearly show that the proposed approach outperforms the baseline methods with a significant margin.