Machine learning for information retrieval

Abstract

In recent years, we have witnessed successful application of machine learning techniques to a wide range of information retrieval problems, including Web search engines, recommendation systems, online advertising, etc. It is thus critical for researchers in the information retrieval community to understand the core machine learning techniques. In order to accommodate audiences with different levels of understanding of machine learning, we divide this tutorial into two sessions: the first session will focus on basic machine learning concepts and tools; in the second session, we will introduce more advanced topics in machine learning, and will present recent developments in machine learning and its application to information retrieval. Each season is self-contained. Session 1: Core Learning Technologies for Information Retrieval. This session of the tutorial will cover the core machine learning methods, basic optimization techniques and key information retrieval applications. In particular, it includes: 1). Core concepts in machine learning, such as supervised learning/unsupervised learning, bias and variance trade off, and probabilistic models; 2). Useful concepts and algorithms in optimization including the first and second order gradient methods, and Expectation and Maximization; 3). The application of machine learning methods to key information retrieval problems including text classification, collaborative filtering, clustering and learning to rank; Session 2: Emerging Learning Technologies for Information Retrieval. This session will cover more advanced machine learning techniques that have started to be utilized in information retrieval applications. In particular, it will cover: 1). Advanced Optimization Techniques including stochastic optimization and smooth minimization; 2). Emerging Learning Techniques such as Multiple-Instance Learning, Active Learning and Semi-supervised Learning. The tutorial will benefit a large body of audience in the information retrieval community, ranging from students who are new to machine learning to the seasoned researchers who would like to understand the recent advance in machine learning for information retrieval research. This tutorial will also benefit the practitioners who apply learning techniques to real-world information retrieval systems.