Movie recommender based on visual content analysis using deep learning techniques

Nowadays there is a growing interest in the artificial intelligence sector and its varied applications allowing solve problems that for humans are very intuitive and nearly automatic, but for machines are very complicated. One of these problems is the automatic recommendation of multimedia content. In this context, the work proposed try to exploit Computer Vision and Deep Learning techniques for content analysis in video. Based on intermediate extracted information a recommendation engine will be developed allowing the inclusion of learning algorithms using as base data trailers of the films. This project is divided into two main parts. After getting the dataset of movie trailers, the first part of the project consists of the extraction of characteristics from different trailers. For this purpose, computer vision techniques and deep learning architectures will be used. The set of algorithms goes from computer vision tasks as the analysis of color histograms and optical ow to complex analysis of actions or object detectors based on Deep Learning algorithms. The second part of the project is the recommender engine. For the recommender, different machine learning and Deep learning methods will be put into practice in order to learn efficiently about correlations between data. This recommender will be trained using neural networks over the proposed selected dataset. Three different options will be made with three different architectures for the recommender engine. The first will be a simple sequential neural network, the second an autoencoder and the third a double autoencoder. To compare the results of the three options, objective metrics (MSE, MAE, precision) and subjective metrics (polls) will be used. The final output of the project is provide from one input trailer, the ten best matches only based on the content analysis and the trained recommender