Large Language Models (LLMs) have demonstrated remarkable abilities in understanding and generating language, inspiring their adoption in various domains beyond NLP. In this tutorial, we explore how LLMs can be leveraged to enhance sequential recommendation systems. We begin by introducing key LLM concepts relevant to this domain, followed by a structured presentation of increasingly advanced approaches, including semantic embeddings, text-rich modeling, latent relations, long-textual behavior modeling, hierarchical item-user architectures, model distillation, cross-domain generalization, and user-centric personalization. We also include live code walkthroughs that illustrate core ideas and real-world implementations.

Deep Reinforcement Learning uses best of both Reinforcement Learning and Deep Learning for solving problems which cannot be addressed by them individually. Deep Reinforcement Learning has been used widely for games, robotics etc. Limited work has been done for applying Deep Reinforcement Learning for Conversational AI. Hence, in this tutorial cover application of Deep Reinforcement Learning for Conversational AI.

Deep Reinforcement Learning (DRL) uses the best of both Reinforcement Learning and Deep Learning for solving problems which cannot be addressed by them individually. Deep Reinforcement Learning has been used widely for games, robotics etc. Limited work has been done for applying DRL for Conversational Recommender System (CRS). Hence, this tutorial covers the application of DRL for CRS. We give conceptual introduction to Reinforcement Learning and Deep Reinforcement Learning and cover Deep Q-Network, Dyna, REINFORCE and Actor Critic methods. We then cover various real life case studies with increasing complexity starting from CRS, deep CRS, adaptivity, topic guided CRS, deep and large-scale CRSs. We plan to share pre-read for Reinforcement Learning and Deep Reinforcement learning so that participants can grasp the material well.

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).

Recommender Systems fuel e-commerce. Deep Learning techniques have started to make an impact in building recommenders. Many techniques have been proposed recently to create low dimensional embeddings of heterogeneous sources including users, items, text and images that can capture the semantic relationships between them. Such combined embeddings play a very important role in the effectiveness of a Recommender System.