TY - GEN
T1 - Click is Not Equal to Purchase
T2 - 23rd International Conference on Web Information Systems Engineering, WISE 2021
AU - Zhang, Huiwang
AU - Zhao, Pengpeng
AU - Xian, Xuefeng
AU - Sheng, Victor S.
AU - Hao, Yongjing
AU - Cui, Zhiming
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Reinforcement learning (RL) has achieved ideal performance in recommendation systems (RS) by taking care of both immediate and future rewards from users. However, the existing RL-based recommendation methods assume that only a single type of interaction behavior (e.g., clicking) exists between user and item, whereas practical recommendation scenarios involve multiple types of user interaction behaviors (e.g., adding to cart, purchasing). In this paper, we propose a Multi-Task Reinforcement Learning model for multi-behavior Recommendation (MTRL4Rec), which gives different actions for users’ different behaviors with a single agent. Specifically, we first introduce a modular network in which modules can be reused or isolated from each other to model the commonalities and differences between users’ behaviors. Then a task routing network is used to generate routes in the modular network for each behavior task. Finally, we adopt a hierarchical reinforcement learning architecture to improve the efficiency of MTRL4Rec. Experiments on two public datasets validated the effectiveness of MTRL4Rec.
AB - Reinforcement learning (RL) has achieved ideal performance in recommendation systems (RS) by taking care of both immediate and future rewards from users. However, the existing RL-based recommendation methods assume that only a single type of interaction behavior (e.g., clicking) exists between user and item, whereas practical recommendation scenarios involve multiple types of user interaction behaviors (e.g., adding to cart, purchasing). In this paper, we propose a Multi-Task Reinforcement Learning model for multi-behavior Recommendation (MTRL4Rec), which gives different actions for users’ different behaviors with a single agent. Specifically, we first introduce a modular network in which modules can be reused or isolated from each other to model the commonalities and differences between users’ behaviors. Then a task routing network is used to generate routes in the modular network for each behavior task. Finally, we adopt a hierarchical reinforcement learning architecture to improve the efficiency of MTRL4Rec. Experiments on two public datasets validated the effectiveness of MTRL4Rec.
KW - Multi-behavior
KW - Multi-task
KW - Recommendation system
KW - Reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85142720927&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-20891-1_32
DO - 10.1007/978-3-031-20891-1_32
M3 - Conference contribution
AN - SCOPUS:85142720927
SN - 9783031208904
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 443
EP - 459
BT - Web Information Systems Engineering – WISE 2022 - 23rd International Conference, Proceedings
A2 - Chbeir, Richard
A2 - Huang, Helen
A2 - Silvestri, Fabrizio
A2 - Manolopoulos, Yannis
A2 - Zhang, Yanchun
A2 - Zhang, Yanchun
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 1 November 2022 through 3 November 2022
ER -