Machine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them. The fast pace of modernday research has given rise to many di erent approaches for many di erent IR problems. The amount of information available can be overwhelming both for junior students and for experienced researchers looking for new research topics and directions. Additionally, it is interesting to see what key insights into IR problems the new technologies are able to give us. The aim of this full-day tutorial is to give a clear overview of current tried-and-trusted neural methods in IR and how they bene t IR research. It covers key architectures, as well as the most promising future directions.
MOTIVATIONPrompted by the advances of deep learning in computer vision research, neural networks have resurfaced as a popular machine learning paradigm in many other directions of research as well, including information retrieval. Recent years have seen neural networks being applied to all key parts of the typical modern IR pipeline, such core ranking algorithms [26,42,51], click models [9,10], knowledge graphs [8,35], text similarity [28,47], entity retrieval [52,53], language modeling [5], question answering [22,56], and dialogue systems [34,54].A key advantage that sets neural networks apart from many learning strategies employed earlier, is their ability to work from raw input data. E.g., when given enough training data, well-designed networks can become feature extractors themselves, e.g., incorporating basic input characteristics such as term frequency (tf) and term saliency (idf)-that used to be pre-calculated o ine-in their initial layers. Where designing features used to be a crucial aspect and contribution of newly proposed IR approaches, the focus * Corresponding author.SIGIR '17, Shinjuku, Tokyo, Japan has shifted to designing network architectures instead. As a consequence, many di erent architectures and paradigms have been proposed, such as auto-encoders, recursive networks, recurrent networks, convolutional networks, various embedding methods, deep reinforcement and deep q-learning, and, more recently, generative adversarial networks, of which most have been applied in IR settings. The aim of the neural networks for IR (NN4IR) tutorial is to provide a clear overview of the main network architectures currently applied in IR and to show explicitly how they relate to previous work. The tutorial covers methods applied 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 and tricks.We expect the tutorial to be useful both for academic and industrial researchers and practitioners who either 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.
OBJECTIVESThe material in the tutorial covers a broad range of IR applications. It is structured as follows:Preliminaries (60 minutes). The rece...
