Quad-tier entity fusion contrastive representation learning for knowledge aware recommendation system

Quad-tier entity fusion contrastive representation learning for knowledge aware recommendation system

Knowledge graph (KG) has recently emerged as a powerful source of auxiliary information in the realm of knowledge-aware recommendation (KGR) systems. However, due to the lack of supervision signals caused by the sparse nature of user-item interactions, existing supervised graph neural network (GNN)...

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Bibliographic Details
Main Authors: Ong, Kenneth Rongqing, Qiu, Wei, Khong, Andy Wai Hoong
Other Authors: School of Electrical and Electronic Engineering
Format: Conference Paper
Language:English
Published: 2024
Subjects:
Computer and Information Science
Recommender system
Knowledge graph
Users preferences
Contrastive learning
Online Access:https://hdl.handle.net/10356/175884

Internet

https://hdl.handle.net/10356/175884

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