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We thank the reviewers for their careful feedback and kind comments. We respond to the comments by each reviewer in detail below: Reviewer_1: Regarding our optimization innovations: In addition to what was noted by the reviewer, another major contribution of our work is the efficient Hessian vector multiplication schemes (Algorithms 1 and 2) that enable the use of conjugate gradient methods to be applied in the collaborative filtering setting. The Hessian vector multiplication methods coupled with conjugate gradient makes GRALS highly efficient, much more so than just a direct application of conjugate gradient schemes. Reviewer_2: 1. Regarding lack of comparison to the 3 suggested papers: Salakhutdinov and Mnih, Probabilistic Matrix Factorization [NIPS 2008], Mackey et al., Divide-and-Conquer Matrix Factorization [NIPS 2011], Lee et al., Local Low-Rank Matrix Approximation [ICML 2013]. The SGD method we compare to in the paper was proposed in Zhou et al. 2012, which extends the work of Salakhutdinov and Minh to the kernel setting.