TY - JOUR
T1 - Inter-Basket and Intra-Basket Adaptive Attention Network for Next Basket Recommendation
AU - Che, Binbin
AU - Zhao, Pengpeng
AU - Fang, Junhua
AU - Zhao, Lei
AU - Sheng, Victor S.
AU - Cui, Zhiming
N1 - Funding Information:
This work was supported in part by the NSFC under Grant 61876217, Grant 61872258, and Grant 61728205, in part by the Suzhou Science and Technology Development Program under Grant SYG201803, in part by the Postdoctoral Research Foundation of China under Grant 2017M621813, in part by the Natural Science Fund for Colleges and Universities in Jiangsu Province under Grant 18KJB520044, and in part by the Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, under Grant IIP2019-1.
Publisher Copyright:
© 2013 IEEE.
PY - 2019
Y1 - 2019
N2 - Next basket recommendation with consideration of user sequential shopping behaviors plays a significant role in E-commerce to improve the user experience and service quality. Recently, recurrent neural networks (RNNs), especially attention-based RNN, have been widely adopted in the next basket recommendation. However, existing fixed attention mechanisms are not designed to model the dynamic and diverse characteristics of user appetites. In this paper, we propose an inter-basket and intra-basket adaptive attention network (IIAAN) for the next basket recommendation. Specifically, the inter-basket adaptive attention acts on all historical user baskets to model user's diverse long-term preferences, while the intra-basket adaptive attention is designed to act on item-level in the most recent basket to model user's dynamic and different short-term preferences. Then, we further integrate inter-basket and intra-basket adaptive attentions together to improve recommendation effectiveness. Finally, we evaluate the proposed model IIAAN using three real-world datasets from various E-commerce platforms. Our experimental results show that our model IIAAN significantly outperforms the state-of-the-art approaches for the next basket recommendation.
AB - Next basket recommendation with consideration of user sequential shopping behaviors plays a significant role in E-commerce to improve the user experience and service quality. Recently, recurrent neural networks (RNNs), especially attention-based RNN, have been widely adopted in the next basket recommendation. However, existing fixed attention mechanisms are not designed to model the dynamic and diverse characteristics of user appetites. In this paper, we propose an inter-basket and intra-basket adaptive attention network (IIAAN) for the next basket recommendation. Specifically, the inter-basket adaptive attention acts on all historical user baskets to model user's diverse long-term preferences, while the intra-basket adaptive attention is designed to act on item-level in the most recent basket to model user's dynamic and different short-term preferences. Then, we further integrate inter-basket and intra-basket adaptive attentions together to improve recommendation effectiveness. Finally, we evaluate the proposed model IIAAN using three real-world datasets from various E-commerce platforms. Our experimental results show that our model IIAAN significantly outperforms the state-of-the-art approaches for the next basket recommendation.
KW - Basket recommendation
KW - adaptive attention
KW - recurrent neural network
UR - http://www.scopus.com/inward/record.url?scp=85068971052&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2019.2922985
DO - 10.1109/ACCESS.2019.2922985
M3 - Article
AN - SCOPUS:85068971052
SN - 2169-3536
VL - 7
SP - 80644
EP - 80650
JO - IEEE Access
JF - IEEE Access
M1 - 8736740
ER -