TY - JOUR
T1 - UBAR
T2 - User Behavior-Aware Recommendation with knowledge graph
AU - Wu, Xing
AU - Li, Yisong
AU - Wang, Jianjia
AU - Qian, Quan
AU - Guo, Yike
N1 - Funding Information:
This work is supported by the National Natural Science Foundation of China (Grant No. 62172267), the National Key R&D Program of China (Grant No. 2019YFE0190500), the Natural Science Foundation of Shanghai, China (Grant No. 20ZR1420400), the State Key Program of National Natural Science Foundation of China (Grant No. 61936001), the Shanghai Pujiang Program, China (Grant No. 21PJ1404200), the Key Research Project of Zhejiang Laboratory, China (No. 2021PE0AC02).
Publisher Copyright:
© 2022 Elsevier B.V. All rights reserved.
PY - 2022/10/27
Y1 - 2022/10/27
N2 - The recommendation system is widely used in many aspects of digital economy to offer personalized services, in which efficient capture of user–item relations is of critical importance. However, there are two inevitable challenges in this task. On the one hand, the extraction of complicated associations is not easy among multiple users’ actions such as searching, browsing or purchasing. On the other hand, the integration of numerous items’ connections is indispensable for the recommendation framework. To address the stated challenges, we propose a User Behavior-Aware Recommendation method with knowledge graph (UBAR) consisting of a user behavior-aware module and an item knowledge graph module. The performance of the proposed UBAR method is evaluated on four datasets (i.e., Tmall, Taobao, Amazon, and Movie-Lens), and the experimental results demonstrate that the proposed UBAR outperforms state-of-the-art methods. The qualitative and quantitative analysis proves the effectiveness and efficiency of the proposed UBAR method.
AB - The recommendation system is widely used in many aspects of digital economy to offer personalized services, in which efficient capture of user–item relations is of critical importance. However, there are two inevitable challenges in this task. On the one hand, the extraction of complicated associations is not easy among multiple users’ actions such as searching, browsing or purchasing. On the other hand, the integration of numerous items’ connections is indispensable for the recommendation framework. To address the stated challenges, we propose a User Behavior-Aware Recommendation method with knowledge graph (UBAR) consisting of a user behavior-aware module and an item knowledge graph module. The performance of the proposed UBAR method is evaluated on four datasets (i.e., Tmall, Taobao, Amazon, and Movie-Lens), and the experimental results demonstrate that the proposed UBAR outperforms state-of-the-art methods. The qualitative and quantitative analysis proves the effectiveness and efficiency of the proposed UBAR method.
KW - Knowledge graph
KW - Recommendation system
KW - User behavior-aware
KW - User–item relations
UR - http://www.scopus.com/inward/record.url?scp=85136083276&partnerID=8YFLogxK
U2 - 10.1016/j.knosys.2022.109661
DO - 10.1016/j.knosys.2022.109661
M3 - Journal article
AN - SCOPUS:85136083276
SN - 0950-7051
VL - 254
JO - Knowledge-Based Systems
JF - Knowledge-Based Systems
M1 - 109661
ER -