Before revision:  As a result, brute-force matrix multiply outperforms recent proposals for top-K serving on several collaborative filtering tasks. 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.
After revision:  As a result, brute-force matrix multiplication: these models may have significantly less similarity than can be exploited by these techniques. To address this problem, we propose RecOpt, a system that uses an efficient, sampling-based estimation technique to automatically choose between indexing or brute force. In addition, we propose a new baseline indexing scheme, RecDex, that can leverage blocked linear algebra to improve indexing-based serving performance. Together, RecOpt and RecDex outperform state-of-the-art indexes by 3.2\times on average, and low-similarity regimes.