Exploiting Deep Learning and Explanation Methods for Movie Tag Prediction

Indexing multimedia content with rich and accurate metadata allows for improving the quality of the search engines' results and boosting the recommender systems performances, which can benefit from this information to yield more effective recommendation lists. Therefore, the adoption of tools able to automatically label multimedia content with informative tags represents an important task for all the companies offering streaming entertainment services. However, domain experts generally perform the tagging process manually, making it time-consuming and error-prone. In the last few years, Machine Learning techniques have been proposed as a promising solution to automate this type of task, but the lack of clean and labeled training data hinders the learning of robust classification models. To cope with the issues described above, in this work, we devised a Deep Learning based solution for semi-automatic multi-label classification integrating post-hoc explanation techniques. Specifically, model explanation methods are exploited to assist the operator in the labeling process by facilitating an understanding of the model predictions. The proposed approach has been validated on a real dataset, and the experimental results demonstrate its effectiveness.