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Imagine you are a teacher attempting to assess a student's level in aparticular subject. If you design a test with only hard questions, and thestudent fails, this mostly proves that the student does not understand the moreadvanced material. A more insightful exam would include different types ofquestions varying in difficulty to truly understand the student's weaknessesand strengths from different perspectives. In the field of Recommender Systems(RS), more often than not, we design evaluations to measure an algorithm'sability to optimize goals in complex scenarios, representative of thereal-world challenges the system would most probably face. Nevertheless, thispaper posits that testing an algorithm's ability to address both simple andcomplex tasks/problems would offer a more detailed view of performance to helpidentify, at a more granular level, the weaknesses and strengths of solutionswhen facing different scenarios/domains. We believe the RS community wouldgreatly benefit from creating a collection of standardized, simple, andtargeted experiments, which, much like a suite of "unit tests", wouldindividually assess an algorithm's ability to tackle core challenges that makeup complex RS tasks. What's more, these experiments go beyond traditionalpass/fail "unit tests". Running an algorithm against the collection ofexperiments allows a researcher to empirically analyze in which type ofsettings an algorithm performs best and to what degree under different metrics.Not only do we defend this position, in this paper, we also offer a proposal ofhow these simple and targeted experiments could be defined and shared andsuggest potential next steps to make this project a reality.

Towards Creating a Standardized Collection of Simple and Targeted Experiments to Analyze Core Aspects of the Recommender Systems Problem