Initialization matters : regularizing manifold-informed initialization for neural recommendation systems

Initialization matters : regularizing manifold-informed initialization for neural recommendation systems

Proper initialization is crucial to the optimization and the generalization of neural networks. However, most existing neural recommendation systems initialize the user and item embeddings randomly. In this work, we propose a new initialization scheme for user and item embeddings called Laplacian E...

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Bibliographic Details
Main Authors: Zhang, Yinan, Li, Boyang, Liu, Yong, Wang, Hao, Miao, Chunyan
Other Authors: School of Computer Science and Engineering
Format: Conference Paper
Language:English
Published: 2021
Subjects:
Engineering::Computer science and engineering
Network Initialization
Recommender Systems
Online Access:https://hdl.handle.net/10356/153528

Internet

https://hdl.handle.net/10356/153528

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