✖ Differential Privacy for Information Retrieval by Grace Hui Yang and Sicong Zhang

Information Retrieval (IR) research has extensively utilized personalization to advance its state-of-the-art. In this process, many IR algorithms and applications require the use of users’ personal information, contextual information and other sensitive and private information. However, while IR researchers are making progress, there is always a concern of how not to violate the users’ privacy. Sometimes, the concern becomes so overwhelming that the IR research has to be stopped to avoid leaking of users’ privacy. The good news is that recently increasing attentions have been paid on the joint field of privacy and IR – privacy-preserving IR. As part of the effort, this tutorial offers an introduction to differential privacy (DP), one of the most advanced techniques in privacy research, and provides necessary set of theoretical knowledge for applying privacy techniques in IR. Differential privacy is a technique that provides strong privacy guarantees for data protection. Theoretically, it aims to maximize the data utility in statistical datasets while minimizing the risk of exposing individual data entries to any adversary. Differential privacy has been applied across of a wide range of applications in database, data mining, and IR. This tutorial aims to lay a theoretical foundation of DP and how it can be applied to IR.

✖ Bandit Algorithms in Interactive Information Retrieval by Dorota Glowacka

The multi-armed bandit problem models an agent that simultaneously attempts to acquire new knowledge (exploration) and optimize his decisions based on existing knowledge (exploitation). The agent attempts to balance these competing tasks in order to maximize his total value over the period of time considered. There are many practical applications of the bandit model, such as clinical trials, adaptive routing or portfolio design. Over the last decade there has been an increased interest in the development of new bandit algorithms for specific problems in information, such as diverse document ranking, news recommendation or ranker evaluation. The aim of this tutorial is to provide an overview of the various applications of bandit algorithms in information retrieval as well as issues related to their practical deployment and performance in real-life systems/applications. 

Efficiency/Effectiveness Trade-offs in Learning to Rank by Claudio Lucchese and Franco Maria Nardini

Tutorial on the effciency/effectiveness tradeoffs in Learning to Rank. In the last years, learning to rank (LtR) had a significant influence on several tasks in the Information Retrieval field, with large research efforts coming both from the academia and the industry. Indeed, efficiency requirements must be fulfilled in order to make an effective research product deployable within an industrial environment. The evaluation of a model can be too expensive due to its size, the features used and several other factors. This tutorial discusses the recent solutions that allow to build an effective ranking model that satisfies temporal budget constrains at evaluation time. For more information please visit the tutorial website.




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