- Brian St Thomas
- Christine Hosey
- Fernando Diaz
- Praveen Ravichandran
Designers of online search and recommendation services often need to develop metrics to assess system performance. This tutorial focuses on mixed methods approaches to developing user-focused evaluation metrics. This starts with choosing how data is logged or how to interpret current logged data, with a discussion of how qualitative insights and design decisions can restrict or enable certain types of logging. When we create a metric from that logged data, there are underlying assumptions about how users interact with the system and evaluate those interactions. We will cover what these assumptions look like for some traditional system evaluation metrics and highlight quantitative and qualitative methods that investigate and adapt these assumptions to be more explicit and expressive of genuine user behavior. We discuss the role that mixed methods teams can play at each stage of metric development, starting with data collection, designing both online and offline metrics, and supervising metric selection for decision making. We describe case studies and examples of these methods applied in the context of evaluating personalized search and recommendation systems. Finally, we close with practical advice for applied quantitative researchers who may be in the early stages of planning collaborations with qualitative researchers for mixed methods metrics development.
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