2024/09/23 by Lukas Wegmeth, Tobias Vente, Wegmeth, Lukas +5 · 2 citations
Computer Science · Engineering · #Data Stream Mining Techniques #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Smart Grid Energy Management #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.2409.15060
openalex publication_date 2024/09/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Due to recent advancements in machine learning, recommender systems use increasingly more energy for training, evaluation, and deployment. However, the recommender systems community often does not report the energy consumption of their experiments. In today's research landscape, no tools exist to easily measure the energy consumption of recommender systems experiments. To bridge this gap, we introduce EMERS, the first software library that simplifies measuring, monitoring, recording, and sharing the energy consumption of recommender systems experiments. EMERS measures energy consumption with smart power plugs and offers a user interface to monitor and compare the energy consumption of recommender systems experiments. Thereby, EMERS improves sustainability awareness and simplifies self-reporting energy consumption for recommender systems practitioners and researchers.