New Metrics to Encourage Innovation and Diversity in Information Retrieval Approaches
Mehmet Deniz T\"urkmen, Matthew Lease, Mucahid Kutlu

TL;DR
This paper introduces new IR evaluation metrics designed to promote diversity and innovation by rewarding systems that explore divergent strategies and find unique relevant documents, thus encouraging more radical approaches.
Contribution
It proposes a novel class of IR metrics that incentivize exploration of diverse, high-risk strategies, contrasting with traditional metrics focused on user experience.
Findings
Metrics change system rankings to favor innovative approaches.
Metrics reward systems that discover rare, relevant documents.
Metrics demonstrate higher stability and discriminative power.
Abstract
In evaluation campaigns, participants often explore variations of popular, state-of-the-art baselines as a low-risk strategy to achieve competitive results. While effective, this can lead to local "hill climbing" rather than more radical and innovative departure from standard methods. Moreover, if many participants build on similar baselines, the overall diversity of approaches considered may be limited. In this work, we propose a new class of IR evaluation metrics intended to promote greater diversity of approaches in evaluation campaigns. Whereas traditional IR metrics focus on user experience, our two "innovation" metrics instead reward exploration of more divergent, higher-risk strategies finding relevant documents missed by other systems. Experiments on four TREC collections show that our metrics do change system rankings by rewarding systems that find such rare, relevant…
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Taxonomy
TopicsInformation Retrieval and Search Behavior · Misinformation and Its Impacts · Expert finding and Q&A systems
