  Position bias, i.e., users' preference of an item is affected by its placing
position, is well studied in the recommender system literature. However, most
existing methods ignore the widely coupled ranking bias, which is also related
to the placing position of the item. Using both synthetic and industrial
datasets, we first show how this widely coexisted ranking bias deteriorates the
performance of the existing position bias estimation methods. To mitigate the
position bias with the presence of the ranking bias, we propose a novel
position bias estimation method, namely gradient interpolation, which fuses two
estimation methods using a fusing weight. We further propose an adaptive method
to automatically determine the optimal fusing weight. Extensive experiments on
both synthetic and industrial datasets demonstrate the superior performance of
the proposed methods.
