A RNN-based multi-factors model for repeat consumption prediction

Abstract

Consumption is a common activity in people’s daily life, and some reports show that repeat consumption even accounts for a greater portion of people’s observed activities compared with novelty-seeking consumption. Therefore, modeling repeat consumption is a very important study to understand human behavior. In this paper, we proposed a multi-factors RNN (MF-RNN) model to predict the users’ repeat consumption behavior. We analysed some factors which can influence customers’ daily repeat consumption and introduced those factor in MF-RNN model to predict the users’ repeat consumption behavior. An empirical study on real-world data sets shows encouraging results on our approach. In the real-world dataset, the MF-RNN gets good prediction performance, better than Most Frequent, HMM, Recency, DYRC and LSTM methods. We compared the effect of different factors on the customers’ repeat consumption behavior, and found that the MF-RNN gets better performance than non-factor RNN. Besides, we analyzed the differences in consumption behaviors between different cities and different regions in China.