When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions-Reference-Cited by-同舟云学术

When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions

Published:2024-05-13 Issue: Volume:13 Page:3632-3642
ISSN:
Container-title:Proceedings of the ACM Web Conference 2024
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Author:

Zhang Chunxu¹^ORCID,Long Guodong²^ORCID,Zhou Tianyi³^ORCID,Zhang Zijian⁴^ORCID,Yan Peng²^ORCID,Yang Bo¹^ORCID

Affiliation:

1. College of Computer Science and Technology, Jilin University & Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China

2. Australian Artificial Intelligence Institute, FEIT, University of Technology Sydney, Sydney, NSW, Australia

3. Computer Science and UMIACS, University of Maryland, Maryland, MD, USA

4. College of Computer Science and Technology, Jilin University & City University of Hong Kong, Changchun, China

Funder

the National Natural Science Foundation of China

the National Key R&D Program of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3589334.3645525

Reference48 articles.

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4. HRS-CE: A hybrid framework to integrate content embeddings in recommender systems for cold start items

5. JesúS Bobadilla, Fernando Ortega, Antonio Hernando, and Jesús Bernal. 2012. A collaborative filtering approach to mitigate the new user cold start problem. Knowledge-based systems, Vol. 26 (2012), 225--238.

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