  Pure methods generally perform excellently in either recommendation accuracy
or diversity, whereas hybrid methods generally outperform pure cases in both
recommendation accuracy and diversity, but encounter the dilemma of optimal
hybridization parameter selection for different recommendation focuses. In this
article, based on a user-item bipartite network, we propose a data
characteristic based algorithm, by relating the hybridization parameter to the
data characteristic. Different from previous hybrid methods, the present
algorithm adaptively assign the optimal parameter specifically for each
individual items according to the correlation between the algorithm and the
item degrees. Compared with a highly accurate pure method, and a hybrid method
which is outstanding in both the recommendation accuracy and the diversity, our
method shows a remarkably promotional effect on the long-standing challenging
problem of the cold start, as well as the recommendation diversity, while
simultaneously keeps a high overall recommendation accuracy. Even compared with
an improved hybrid method which is highly efficient on the cold start problem,
the proposed method not only further improves the recommendation accuracy of
the cold items, but also enhances the recommendation diversity. Our work might
provide a promising way to better solving the personal recommendation from the
perspective of relating algorithms with dataset properties.
