TY - GEN
T1 - Adaptive attention-aware gated recurrent unit for sequential recommendation
AU - Luo, Anjing
AU - Zhao, Pengpeng
AU - Liu, Yanchi
AU - Xu, Jiajie
AU - Li, Zhixu
AU - Zhao, Lei
AU - Sheng, Victor S.
AU - Cui, Zhiming
N1 - Funding Information:
Acknowledgement. This research was partially supported by the NSFC (61876117, 61876217, 61872258, 61728205), the Suzhou Science and Technology Development Program (SYG2 01803) and the Open Program of Neusoft Corporation (SKLSAOP1801).
Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - Due to the dynamic and evolutionary characteristics of user interests, sequential recommendation plays a significant role in recommender systems. A fundamental problem in the sequential recommendation is modeling dynamic user preference. Recurrent Neural Networks (RNNs) are widely adopted in the sequential recommendation, especially attention-based RNN becomes the state-of-the-art solution. However the existing fixed attention mechanism is insufficient to model the dynamic and evolutionary characteristics of user sequential preferences. In this work, we propose a novel solution, Adaptive Attention-Aware Gated Recurrent Unit (3AGRU), to learn adaptive user sequential representations for sequential recommendation. Specifically, we adopt an attention mechanism to adapt the representation of user sequential preference, and learn the interaction between steps and items from data. Moreover, in the first level of 3AGRU, we construct adaptive attention network to describe the relevance between input and the candidate item. In this way, a new input based on adaptive attention can reflect users’ diverse interests. Then, the second level of 3AGRU applies adaptive attention network to hidden state level to learn a deep user representation which is able to express diverse interests of the user. Finally, we evaluate the proposed model using three real-world datasets from various application scenarios. Our experimental results show that our model significantly outperforms the state-of-the-art approaches on sequential recommendation.
AB - Due to the dynamic and evolutionary characteristics of user interests, sequential recommendation plays a significant role in recommender systems. A fundamental problem in the sequential recommendation is modeling dynamic user preference. Recurrent Neural Networks (RNNs) are widely adopted in the sequential recommendation, especially attention-based RNN becomes the state-of-the-art solution. However the existing fixed attention mechanism is insufficient to model the dynamic and evolutionary characteristics of user sequential preferences. In this work, we propose a novel solution, Adaptive Attention-Aware Gated Recurrent Unit (3AGRU), to learn adaptive user sequential representations for sequential recommendation. Specifically, we adopt an attention mechanism to adapt the representation of user sequential preference, and learn the interaction between steps and items from data. Moreover, in the first level of 3AGRU, we construct adaptive attention network to describe the relevance between input and the candidate item. In this way, a new input based on adaptive attention can reflect users’ diverse interests. Then, the second level of 3AGRU applies adaptive attention network to hidden state level to learn a deep user representation which is able to express diverse interests of the user. Finally, we evaluate the proposed model using three real-world datasets from various application scenarios. Our experimental results show that our model significantly outperforms the state-of-the-art approaches on sequential recommendation.
KW - Adaptive attention mechanism
KW - GRU
KW - Recommender system
UR - http://www.scopus.com/inward/record.url?scp=85065507299&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-18579-4_19
DO - 10.1007/978-3-030-18579-4_19
M3 - Conference contribution
AN - SCOPUS:85065507299
SN - 9783030185787
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 317
EP - 332
BT - Database Systems for Advanced Applications - 24th International Conference, DASFAA 2019, Proceedings
A2 - Yang, Jun
A2 - Tong, Yongxin
A2 - Natwichai, Juggapong
A2 - Gama, Joao
A2 - Li, Guoliang
PB - Springer-Verlag
Y2 - 22 April 2019 through 25 April 2019
ER -