  Deep learning-based recommendation models are used pervasively and broadly,
for example, to recommend movies, products, or other information most relevant
to users, in order to enhance the user experience. Among various application
domains which have received significant industry and academia research
attention, such as image classification, object detection, language and speech
translation, the performance of deep learning-based recommendation models is
less well explored, even though recommendation tasks unarguably represent
significant AI inference cycles at large-scale datacenter fleets. To advance
the state of understanding and enable machine learning system development and
optimization for the commerce domain, we aim to define an industry-relevant
recommendation benchmark for the MLPerf Training andInference Suites. The paper
synthesizes the desirable modeling strategies for personalized recommendation
systems. We lay out desirable characteristics of recommendation model
architectures and data sets. We then summarize the discussions and advice from
the MLPerf Recommendation Advisory Board.
