  The online shopping behavior has the characteristics of rich granularity
dimension and data sparsity and previous researches on user behavior prediction
did not seriously discuss feature selection and ensemble design. In this paper,
we proposed a SE-Stacking model based on information fusion and ensemble
learning for user purchase behavior prediction. After successfully utilizing
the ensemble feature selection method to screen purchase-related factors, we
used the Stacking algorithm for user purchase behavior prediction. In our
efforts to avoid the deviation of prediction results, we optimized the model by
selecting ten different kinds of models as base learners and modifying relevant
parameters specifically for them. The experiments conducted on a
publicly-available dataset shows that the SE-Stacking model can achieve a
98.40% F1-score, about 0.09% higher than the optimal base models. The
SE-Stacking model not only has a good application in the prediction of user
purchase behavior but also has practical value combining with the actual
e-commerce scene. At the same time, it has important significance for academic
research and the development of this field.
