Abstract
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 Time-LSTM 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 next-basket recommendation. Experiments on two real-world datasets demonstrate that the proposed model outperforms existing baselines.
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Leng, Y., Yu, L., Xiong, J., Xu, G. (2020). Recurrent Convolution Basket Map for Diversity Next-Basket Recommendation. In: Nah, Y., Cui, B., Lee, SW., Yu, J.X., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020. Lecture Notes in Computer Science(), vol 12114. Springer, Cham. https://doi.org/10.1007/978-3-030-59419-0_39
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