Image and Video Understanding for Recommendation and Spam Detection Systems

Image and video-based content has become ever present in a variety of domains like news, entertainment and education. Users typically discover and engage with content via search and recommendation systems. It is also important to serve high quality data to users by filtering out irrelevant or harmful content. Thus, there is an increasing need to leverage the rich information in image and video content in order to power systems for search and recommendation. At the same time, the effectiveness and efficiency of these systems has been accelerated by the availability of large-scale labeled datasets and sophisticated deep learning-based models. This tutorial is aimed at providing an overview of image and video understanding, and their practical applications in the industry. We focus on deep learning-based state of the art techniques for image and video understanding. This includes tasks like image classification and segmentation, image-based content retrieval and video classification. We also focus on applications of these technologies to large-scale recommendation and low quality content detection systems. We present concrete examples from various LinkedIn production systems, and also discuss associated practical challenges.