Reproducibility is a key requirement for scientific progress. It allows the\nreproduction of the works of others, and, as a consequence, to fully trust the\nreported claims and results. In this work, we argue that, by facilitating\nreproducibility of recommender systems experimentation, we indirectly address\nthe issues of accountability and transparency in recommender systems research\nfrom the perspectives of practitioners, designers, and engineers aiming to\nassess the capabilities of published research works. These issues have become\nincreasingly prevalent in recent literature. Reasons for this include societal\nmovements around intelligent systems and artificial intelligence striving\ntowards fair and objective use of human behavioral data (as in Machine\nLearning, Information Retrieval, or Human-Computer Interaction). Society has\ngrown to expect explanations and transparency standards regarding the\nunderlying algorithms making automated decisions for and around us.\n This work surveys existing definitions of these concepts, and proposes a\ncoherent terminology for recommender systems research, with the goal to connect\nreproducibility to accountability. We achieve this by introducing several\nguidelines and steps that lead to reproducible and, hence, accountable\nexperimental workflows and research. We additionally analyze several\ninstantiations of recommender system implementations available in the\nliterature, and discuss the extent to which they fit in the introduced\nframework. With this work, we aim to shed light on this important problem, and\nfacilitate progress in the field by increasing the accountability of research.\n