Following the recent trend of in-memory data processing, it is a usual practice to maintain collaborative filtering data in the main memory when generating recommendations in academic and industrial recommender systems.
In this paper, we study the impact of integer compression techniques for in-memory collaborative filtering data in terms of space and time efficiency. Our results provide relevant observations about when and how to compress collaborative filtering data. First, we observe that, depending on the memory constraints, compression techniques may speed up or slow down the performance of state-of-the art collaborative filtering algorithms. Second, after comparing different compression techniques, we find the Frame of Reference (FOR) technique to be the best option in terms of space and time efficiency under different memory constraints
