Dataset Groups Activity Stream AZM5 Offline evaluations of recommender systems attempt to estimate users’ satisfaction with recommendations using static data from prior user interactions. BibTex: @dataset{Mucun_Tian_and_Michael_D_Ekstrand_2024, abstract = {Offline evaluations of recommender systems attempt to estimate users’ satisfaction with recommendations using static data from prior user interactions.}, author = {Mucun Tian and Michael D. Ekstrand}, doi = {10.57702/cfssej2r}, institution = {No Organization}, keyword = {'Albums', 'Digital Music', 'Recommendations'}, month = {dec}, publisher = {TIB}, title = {AZM5}, url = {https://service.tib.eu/ldmservice/dataset/azm5}, year = {2024} }
