Enhancing recommender systems via data augmentation

Enhancing recommender systems via data augmentation

Recommender systems play an essential role in enhancing user experiences by providing personalized content and suggestions, thereby improving user engagement and satisfaction. However, a major challenge faced by recommender systems is data sparsity, where real-world datasets often lack comprehensive...

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Bibliographic Details
Main Author: Zhang, Lingzi
Other Authors: Miao Chun Yan
Format: Thesis-Doctor of Philosophy
Language:English
Published: Nanyang Technological University 2024
Subjects:
Computer and Information Science
Recommender systems
Data augmentation
Online Access:https://hdl.handle.net/10356/179298

Internet

https://hdl.handle.net/10356/179298

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