Competitions, such as the Netflix Prize and Kaggle, have driven a great deal of research on recommendation engines. However, many e-commerce datasets lack explicit ratings, consisting solely of binary purchase information containing no labeled negative data. Such data requires special consideration and treatment for both model selection and validation of results. In this talk I will describe implementation of a recommendation system for binary purchase data in Spark’s MLlib, compare fitting and prediction benchmarks for various models, and illustrate the performance differences across different scales of big data. Finally, I will share the lessons learned in how to efficiently select and implement the best recommendation model for your dataset.