Modern Theoretical Tools for Designing Information Retrieval System

In the past decade, deep learning has significantly reshaped the landscape of information retrieval (IR). The community has recently begun to notice the potential dangers of overusing less-understood mechanisms and over-simplified assumptions to learn patterns and make decisions. In particular, there is growing concerns on the interpretation, reliability, social impact, and long-term utility of real-world IR systems. Therefore, it has become a pressing issue to bring the IR community comprehensive and systematic tools to understand empirical domain solutions and motivate principled design ideas. We focus on the three pillar stones of modern IR systems: pattern recognition with deep learning, causal inference analysis, and online decision making (with bandits and reinforcement learning). Our objectives are as follows.