  Using heat conduction mechanism on a social network we develop a systematic
method to predict missing values as recommendations. This method can treat very
large matrices that are typical of internet communities. In particular, with an
innovative, exact formulation that accommodates arbitrary boundary condition,
our method is easy to use in real applications. The performance is assessed by
comparing with traditional recommendation methods using real data.
