In response to the escalating ecological challenges that threaten global sustainability, there’s a deep need to investigate alternative methods of commerce, such as rental economies. Like most online commerce, rental or otherwise, a functioning recommender system is crucial for their success. Yet the domain has, until this point, been largely neglected by the recommender system research community.Our dataset, derived from our collaboration with the leading Norwegian fashion rental company [Company name omitted for review process], encompasses 64k transactions, rental histories from 2.2k anonymized users, and 15.8k unique outfits in which each physical item’s attributes and rental history is meticulously tracked. All outfits are listed as individual items or their corresponding item groups, referring to shared designs between the individual items. This approach underlines the novel challenges of rental as compared to more traditional recommender system problems where items are generally interchangeable. Each outfit is accompanied by a set of tags describing some of their attributes. We also provide a total of 50k images displaying across all items along with a set of precomputed zero-shot embeddings.We apply a myriad of common recommender system methods to the dataset to provide a performance baseline. This baseline is performed for the traditional fashion recommender system problem of recommending outfit groups and the novel problem of predicting individual item rental. To our knowledge, this is the first publicly available dataset discussing the domain of clothes rental. It is our hope that the publication of this dataset will serve as a catalyst for a new branch of research for specialized fashion rental recommender systems.The dataset is temporarily hosted at https://anonymous.4open.science/r/clothes_rental_dataset-B1E8/ and Google Cloud at https: //console.cloud.google.com/storage/browser/clothes-rental-datasetThe code for collecting, processing, and evaluating the dataset is hosted at https://anonymous.4open.science/r/Vibrent_Dataset_Collection-03DC/