Concept to code: deep learning for multitask recommendation

Deep Learning has shown significant results in Computer Vision, Natural Language Processing, Speech and recommender systems. Promising techniques include Embedding, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and its variant Long Short-Term Memory (LSTM and Bi-directional LSTMs), Attention, Autoencoders, Generative Adversarial Networks (GAN) and Bidirectional Encoder Representations from Transformer (BERT). Multi-task learning (MTL) has led to successes in many applications of machine learning. We are proposing a tutorial for applying MTL for recommendation, improving recommendation and providing explanation. We cover few recent and diverse techniques which will be used for hands-on session. We believe that a self-contained tutorial giving good conceptual understanding of MTL technique with sufficient mathematical background along with actual code will be of immense help to RecSys participants.