In this paper, we identify a lack of a homogeneous interpretation of the data minimization principle and explore two operational definitions applicable in the context of personalization. The focus of our empirical study in the domain of recommender systems is on providing foundational insights about the (i) feasibility of different data minimization definitions, (ii) robustness of different recommendation algorithms to minimization, and (iii) performance of different minimization strategies. We find that the performance decrease incurred by data minimization might not be substantial,but that it might disparately impact different users—a finding whichhas implications for the viability of different formal minimizationdefinitions. Overall, our analysis uncovers the complexities of thedata minimization problem in the context of personalization andmaps the remaining computational and regulatory challenges

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