KPRLN: deep knowledge preference-aware reinforcement learning network for recommendation
Abstract User preference information plays an important role in knowledge graph-based recommender systems, which is reflected in users having different preferences for each entity–relation pair in the knowledge graph. Existing approaches have not modeled this fine-grained user preference feature wel...
Main Authors: | Di Wu, Mingjing Tang, Shu Zhang, Ao You, Wei Gao |
---|---|
Format: | Article |
Language: | English |
Published: |
Springer
2023-05-01
|
Series: | Complex & Intelligent Systems |
Subjects: | |
Online Access: | https://doi.org/10.1007/s40747-023-01083-7 |
Similar Items
-
Path-guided intelligent switching over knowledge graphs with deep reinforcement learning for recommendation
by: Shaohua Tao, et al.
Published: (2023-06-01) -
Recommendation Algorithm Based on Knowledge Graph to Propagate User Preference
by: Zhisheng Yang, et al.
Published: (2021-05-01) -
Knowledge-Aware Enhanced Network Combining Neighborhood Information for Recommendations
by: Xiaole Wang, et al.
Published: (2023-04-01) -
Graph Neural Network Recommendation Model Integrating User Preferences
by: XIONG Zhong-min, SHU Gui-wen, GUO Huai-yu
Published: (2022-06-01) -
Recommendation Algorithm Based on Knowledge Graph and Tag-aware
by: NING Ze-fei, SUN Jing-yu, WANG Xin-juan
Published: (2021-11-01)