Before revision: We present SimDex, a new technique for serving exact top-K recommendations on matrix factorization models that measures and optimizes for the similarity between users in the model. Previous serving techniques presume a high degree of similarity (e.g., L2 or cosine distance) among users and/or items in MF models; however, as we demonstrate, the most accurate models are not guaranteed to exhibit high similarity. As a result, brute-force matrix multiply outperforms recent proposals for top-K serving on several collaborative filtering tasks.
After revision: We present SimDex, a new technique for serving exact top-K recommendations on matrix factorization models that measures and optimizes for the models to accelerate predictions. In this paper, we show that the current state of the art does not always outperform brute-force matrix multiply outperforms recent proposals for top-K serving on several collaborative filtering tasks.