Neural Networks for Information Retrieval

Recent advances in deep learning have seen neural networks being applied to all key parts of the modern IR pipeline, such as core ranking algorithms, click models, query autocompletion, query suggestion, knowledge graphs, text similarity, entity retrieval, question answering, and dialogue systems. The fast pace of modern-day research has given rise to many different architectures and paradigms, such as auto-encoders, recursive networks, recurrent networks, convolutional networks, various embedding methods, deep reinforcement learning, and, more recently, generative adversarial networks, of which most have been applied to IR settings. The amount of information available can be overwhelming both for junior students and for experienced researchers looking for new research topics and directions. The aim of the tutorial is to provide an overview of the main network architectures currently applied in IR and to show explicitly how they relate to previous work and how they benefit IR research. Additionally, key insights into IR problems that the new technologies give us are provided. The tutorial covers methods employed in industry and academia, with in-depth insights into the underlying theory, core IR tasks, applicability, key assets and handicaps, scalability concerns and practical tips & tricks. We expect the tutorial to be useful both for academic and industrial researchers and practitioners who want to develop new neural models, use them in their own research in other areas or apply the models described here to improve actual IR systems.