  Network-based recommendation algorithms for user-object link predictions have
achieved significant developments in recent years. For bipartite graphs, the
reallocation of resource in such algorithms is analogous to heat spreading
(HeatS) or probability spreading (ProbS) processes. The best algorithm to date
is a hybrid of the HeatS and ProbS techniques with homogenous initial resource
configurations, which fulfills simultaneously high accuracy and large
diversity. We investigate the effect of heterogeneity in initial configurations
on the HeatS+ProbS hybrid algorithm and find that both recommendation accuracy
and diversity can be further improved in this new setting. Numerical
experiments show that the improvement is robust.
