Before revision:  Based on this observation, we develop SimDex, a new technique for serving matrix factorization models that automatically optimizes serving based on the degree of similarity between users, and outperforms existing methods in both the high-similarity and low-similarity regimes. SimDexfirst measures the degree of similarity among users via clustering and uses a cost-based optimizer to either construct an index on the model or defer to blocked matrix multiply. It leverages highly efficient linear algebra primitives in both cases to deliver predictions either from its index or from brute-force multiply. Overall, SimDex runs an averageof 2x and up to 6x faster than highly optimized baselines for the most accurate models on several popular collaborative filtering datasets .
After revision:  Based on this observation, we develop SimDex, a new technique for serving matrix factorization models that automatically optimizes serving based on the degree of similarity between users, and outperforms existing methods in both the high-similarity and up to 6x faster than highly optimized baselines for the most accurate models on several popular collaborative filtering datasets .