This study presents a voice assistant system to support cooking by utilizing smart speakers in Japanese.This system not only speaks the procedures written in recipes point by point but also answers the common questions from users for the specified recipes.The system applies machine comprehension techniques to millions of recipes for answering the common questions in cooking such as "人参はどうした らよいですか(How should I cook carrots?)".Furthermore, several machine-learning techniques are applied to generate better responses to users.

The application of kernel methods to link analysis is explored. In particular, Kandola et al.'s Neumann kernels are shown to subsume not only the co-citation and bibliographic coupling relatedness but also Kleinberg's HITS importance. These popular measures of relatedness and importance correspond to the Neumann kernels at the extremes of their parameter range, and hence these kernels can be interpreted as defining a spectrum of link analysis measures intermediate between co-citation/bibliographic coupling and HITS. We also show that the kernels based on the graph Laplacian, including the regularized Laplacian and diffusion kernels, provide relatedness measures that overcome some limitations of co-citation relatedness. The property of these kernel-based link analysis measures is examined with a network of bibliographic citations. Practical issues in applying these methods to real data are discussed, and possible solutions are proposed.