Towards Extreme Multi-Label classification of Multimedia Content

Providing rich and accurate metadata for indexing media content represents a major issue for enterprises offering streaming entertainment services. Metadata information are usually exploited to boost the search capabilities for relevant contents and as such it can be used by recommendation algorithms for yielding recommendation lists matching user interests. In this context, we investigate the problem of associating suitable labels (or tag) to multimedia contents, that can accurately describe the topics associated with such contents. This task is usually performed by domain experts in a fully manual fashion that makes the overall process time-consuming and susceptible to errors. In this work we propose a Deep Learning based framework for semi-automatic, multi-label and semi-supervised classification. By integrating different data types (e.g., text, images, etc.) the approach allows for tagging media contents with specific labels. A preliminary experimentation conducted on a real dataset demonstrates the quality of the approach in terms of predictive accuracy.