  Heterogeneity of both the source and target objects is taken into account in
a network-based algorithm for the directional resource transformation between
objects. Based on a biased heat conduction recommendation method (BHC) which
considers the heterogeneity of the target object, we propose a heterogeneous
heat conduction algorithm (HHC), by further taking the source object degree as
the weight of diffusion. Tested on three real datasets, the Netflix, RYM and
MovieLens, the HHC algorithm is found to present a better recommendation in
both the accuracy and personalization than two excellent algorithms, i.e., the
original BHC and a hybrid algorithm of heat conduction and mass diffusion
(HHM), while not requiring any other accessorial information or parameter.
Moreover, the HHC even elevates the recommendation accuracy on cold objects,
referring to the so-called cold start problem, for effectively relieving the
recommendation bias on objects with different level of popularity.
