On the Scalability of Graph Kernels Applied to Collaborative Recommenders

We study the scalability of several recent graph kernel-based collaborative recommendation algorithms. We compare the performance of several graph kernel-based recommendation algorithms, focussing on runtime and recommendation accuracy with respect to the reduced rank of the subspace. We inspect the exponential and Laplacian exponential kernels, the resistance distance kernel, the regularized Laplacian kernel, and the stochastic diffusion kernel. Furthermore, we introduce new variants of kernels based on the graph Laplacian which, in contrast to existing kernels, also allow negative edge weights and thus negative ratings. We perform an evaluation on the Netflix Prize rating corpus on prediction and recommendation tasks, showing that dimensionality reduction not only makes prediction faster, but sometimes also more accurate.