Alternative similarity functions for graph kernels

Given a bipartite graph of collaborative ratings, the task of recommendation and rating prediction can be modeled with graph kernels. We interpret these graph kernels as the inverted squared Euclidean distance in a space defined by the underlying graph and show that this inverted squared Euclidean similarity function can be replaced by other similarity functions. We evaluate several such similarity functions in the context of collaborative item recommendation and rating prediction, using the exponential diffusion kernel, the von Neumann kernel, and the random forest kernel as a basis. We find that the performance of graph kernels for these tasks can be increased by using these alternative similarity functions.