Ilya Markov, Aleksandr Chuklin, Maarten de Rijke and Alexey Borisov will give a course on Click Models for Web Search at the Russian Summer School in Information Retrieval (RuSSIR 2016). This course is an extended version of the tutorial given at SIGIR 2015, AINL-ISMW 2015 and WSDM 2016 and is based on the book on Click Models for Web Search.
Click models, probabilistic models of the interaction behavior of search engine users, have been studied extensively by the information retrieval community in recent years. We now have a handful of click models, parameter estimation methods, evaluation principles and applications of click models, that form the building blocks of ongoing research efforts in the area. The time is right to present this material to a broad audience of information retrieval researchers and practitioners.
The course covers a wide range of topics from basic to advanced click models and from click model estimation and evaluation techniques to applications of click models. Most topics are augmented with live demos, where the participants can try the presented material in practice. Also, the course features two practical sessions, where the participants have a chance to implement a basic and an advanced click models using open-source tools and publicly available datasets with click logs. The participants of the course are provided with the authors’ version of the book on click models and the code and data samples for following the demo and practical sessions.
The material of the course is organized as follows. We start with the definition of a click model and an overview of click model applications. Then we give a unified view on basic click models for web search using a common notation and theoretical background. We discuss the main estimation methods for learning click model parameters, present a set of evaluation techniques for measuring the quality of click models, discuss available datasets and tools for working with click models and present a comprehensive experimental comparison of basic click models for web search.
We then focus on the main applications of click models, such as ranking, user simulation, model-based metrics, etc. We also describe the landscape of advanced click models, dealing with complex SERPs, diverse users, non-linear examination patterns, etc. Finally, we present the current trends in click modeling research, namely using neural networks to model clicks and other types of user interactions. We conclude the course by discussing future research directions in the area of click models.