The rise of electronic e-commerce sites and the formation of the user’s online shopping habits, have brought a huge amount of online consumer behavioral data. Mining users’ preferences from these behavioral logs (e.g. clicking data) and then predicting their final consumption choices are of great importance for improving the conversion rate of e-commerce. Along this line, this paper proposes a way of combining users’ behavioral data and choice model to predict which item each user will finally consume. Specifically, we first estimate the optimum substitute in each consumption session by a utility function of users’ behavioral sequences, and then we build a latent factor based choice model (LF-CM) for the consumed items and the substitutes. In this way, the preference of users can be computed and the future consumptions can be predicted. One step further, to make full use of users’ information of choosing and improve the precision of consumption prediction, we also propose a learning-to-rank model (latent factor and sequence based choice model, LFS-CM), which considers all the items in one session. By integrating latent factors and utility function of users’ behavioral sequences, LFS-CM can improve the prediction precision. Finally, we use the real-world dataset of Tmall and evaluate the performance of our methods on a distributed environment. The experimental results show that both LF-CM and LFS-CM perform well in predicting online consumption behaviors.