Recurrent Convolution Basket Map for Diversity Next-Basket Recommendation

Next-basket recommendation plays an important role in both online and offline market. Existing methods often suffer from three challenges: information loss in basket encoding, sequential pattern mining of the shopping history, and the diversity of recommendations. In this paper, we contribute a novel solution called Rec-BMap ("Recurrent Convolution Basket Map"), to address those three challenges. Specifically, we first propose basket map, which encodes not only the items in a basket without losing information, but also static and dynamic properties of the items in the basket. A convolutional neural network followed by the basket map is used to generate basket embedding. Then, a TimeLSTM with time-gate is proposed to learn the sequence pattern from consumer's historical transactions with different time intervals. Finally, a deconvolutional neural network is employed to generate diverse nextbasket recommendation. Experiments on two real-world datasets demonstrate that the proposed model outperforms existing baselines.