  Personalized recommendation attracts a surge of interdisciplinary researches.
Especially, similarity based methods in applications of real recommendation
systems achieve great success. However, the computations of similarities are
overestimated or underestimated outstandingly due to the defective strategy of
unidirectional similarity estimation. In this paper, we solve this drawback by
leveraging mutual correction of forward and backward similarity estimations,
and propose a new personalized recommendation index, i.e., corrected similarity
based inference (CSI). Through extensive experiments on four benchmark
datasets, the results show a greater improvement of CSI in comparison with
these mainstream baselines. And the detailed analysis is presented to unveil
and understand the origin of such difference between CSI and mainstream
indices.
