Deep Learning-Based Sequential Recommender Systems: Concepts, Algorithms, and Evaluations

What is sequential recommendation? What challenges are traditional sequential recommendation models facing? How to address these challenges in sequential recommendation using advanced deep learning (DL) techniques? What factors do affect the performance of a DL-based sequential recommendation system? And how to utilize these factors to improve DL models? In this tutorial, we will carefully answer these questions by combining DL techniques with sequential recommendation, and provide a comprehensive overview of DL-based sequential recommender system. Specifically, we propose a novel classification framework for sequential recommendation tasks, with which we systematically introduce representative DL-based algorithms for different sequential recommendation scenarios. We further summarize the potentially influential factors of DL-based sequential recommendation, and thoroughly demonstrate their effects via a carefully designed experimental framework, which will be of great help to future research.