Before revision:  In this paper, we show that the current state of the art does not always outperform 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 .
After revision:  In this paper, we show that the current state of the art does not always outperform brute-force approach, blocked matrix multiply (BMM), can outperform the state-of-the-art MIPS solvers by over an order of magnitude, for some -- but not all -- inputs. In this paper, we also present a novel MIPS solution, MAXIMUS, that takes advantage of hardware efficiency and pruning of the search space. Like BMM, MAXIMUS is faster than other solvers by up to an order of magnitude, but again only for some inputs. Since no single solution offers the best runtime performance for all inputs, we introduce a new data-dependent optimizer, OPTIMUS, that selects online with minimal overhead the best MIPS solver for a given input .