Tutorial: Modern Theoretical Tools for Understanding and Designing Next-generation Information Retrieval System
Da Xu, Chuanwei Ruan

TL;DR
This paper provides a systematic tutorial on applying modern theoretical tools to improve understanding, design, and production of next-generation information retrieval systems, addressing current gaps in IR theory.
Contribution
It introduces a comprehensive approach to adapt advanced theoretical tools for IR, enhancing understanding and guiding the design of impactful IR systems.
Findings
Systematic adaptation of theoretical tools improves IR system understanding.
Enhanced model expressivity and generalization guarantees for IR.
Guidelines for integrating theory into IR system design.
Abstract
In the relatively short history of machine learning, the subtle balance between engineering and theoretical progress has been proved critical at various stages. The most recent wave of AI has brought to the IR community powerful techniques, particularly for pattern recognition. While many benefits from the burst of ideas as numerous tasks become algorithmically feasible, the balance is tilting toward the application side. The existing theoretical tools in IR can no longer explain, guide, and justify the newly-established methodologies. The consequences can be suffering: in stark contrast to how the IR industry has envisioned modern AI making life easier, many are experiencing increased confusion and costs in data manipulation, model selection, monitoring, censoring, and decision making. This reality is not surprising: without handy theoretical tools, we often lack principled knowledge…
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