Knowledge graph enhanced contextualized attention-based network for responsible user-specific recommendation
Document Type
Journal Article
Publication Title
ACM Transactions on Intelligent Systems and Technology
Volume
15
Issue
4
Publisher
Association for Computing Machinery
School
School of Engineering
Abstract
With ever-increasing dataset size and data storage capacity, there is a strong need to build systems that can effectively utilize these vast datasets to extract valuable information. Large datasets often exhibit sparsity and pose cold start problems, necessitating the development of responsible recommender systems. Knowledge graphs have utility in responsibly representing information related to recommendation scenarios. However, many studies overlook explicitly encoding contextual information, which is crucial for reducing the bias of multi-layer propagation. Additionally, existing methods stack multiple layers to encode high-order neighbor information while disregarding the relational information between items and entities. This oversight hampers their ability to capture the collaborative signal latent in user-item interactions. This is particularly important in health informatics, where knowledge graphs consist of various entities connected to items through different relations. Ignoring the relational information renders them insufficient for modeling user preferences. This work presents an end-to-end recommendation framework named KGCAN (Knowledge Graph Enhanced Contextualized Attention-Based Network), which explicitly encodes both relational and contextual information of entities to preserve the original entity information. Furthermore, a user-specific attention mechanism is employed to capture personalized recommendations. The proposed model is validated on three benchmark datasets through extensive experiments. The experimental results demonstrate that KGCAN outperforms existing knowledge graph based recommendation models. Additionally, a case study from the healthcare domain is discussed, highlighting the importance of attention mechanisms and high-order connectivity in the responsible recommendation system for health informatics.
DOI
10.1145/3641288
Access Rights
subscription content
Comments
Elahi, E., Anwar, S., Shah, B., Halim, Z., Ullah, A., Rida, I., & Waqas, M. (2024). Knowledge graph enhanced contextualized attention-based network for responsible user-specific recommendation. ACM Transactions on Intelligent Systems and Technology, 15(4). https://doi.org/10.1145/3641288