Weighted sequence loss based recurrent model for repurchase recommendation
Pengda Chen, Jian Li · IOP Conference Series Materials Science and Engineering · 2019
Next basket recommendation becomes an increasing concern. Repurchase recommendation, i.e., predicting which products a user will buy again in a user's next order, is a key subproblem. However, most conventional models are not able to extract the whole important features to describe the customer's repurchase process: context information and sequential information. In our work, we firstly utilize the causal dilated convolutions and recurrent neural network to capture context information and sequential information in different ways. Furthermore, the information extracted by causal dilated convolutions and recurrent neural network is combined at each time step for recommendation. More importantly, to effectively adapt the repurchase recommendation, we introduce a weighted sequence loss, which is able to ignore invalid logloss at special time steps to guide the RNN combined with causal dilated convolutions (RCCNN) training. A deep experimentation shows that RCCNN is able to explain the customer repurchase behaviors, and provide reasonable recommendation.