  Heat conduction process has recently found its application in personalized
recommendation [T. Zhou \emph{et al.}, PNAS 107, 4511 (2010)], which is of high
diversity but low accuracy. By decreasing the temperatures of small-degree
objects, we present an improved algorithm, called biased heat conduction (BHC),
which could simultaneously enhance the accuracy and diversity. Extensive
experimental analyses demonstrate that the accuracy on MovieLens, Netflix and
Delicious datasets could be improved by 43.5%, 55.4% and 19.2% compared with
the standard heat conduction algorithm, and the diversity is also increased or
approximately unchanged. Further statistical analyses suggest that the present
algorithm could simultaneously identify users' mainstream and special tastes,
resulting in better performance than the standard heat conduction algorithm.
This work provides a creditable way for highly efficient information filtering.
