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Metric@CustomerN: Evaluating Metrics at a Customer Level in E-Commerce
Accuracy measures such as Recall, Precision, and Hit Rate have been a standard way of evaluating Recommendation Systems.
Towards Creating a Standardized Collection of Simple and Targeted Experiments to Analyze Core Aspects of the Recommender Systems Problem
We believe the RS community would greatly benefit from creating a collection of standardized, simple, and targeted experiments, which, much like a suite of "unit tests", would individually assess an algorithm's ability to tackle core challenges that make up complex RS tasks.
Towards Sharing Task Environments to Support Reproducible Evaluations of Interactive Recommender Systems
Beyond sharing datasets or simulations, we believe the Recommender Systems (RS) community should share Task Environments.
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