Meta learning-based relevant user identification and aggregation for cold-start recommendation
- 21 December 2024
- journal article
- research article
- Published by Springer Nature in Journal of Intelligent Information Systems
- Vol. 63 (3) , 723-744
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China (62272336)
- Natural Science Foundation of Shanxi Province (202103021224286)
- Key Laboratory Project of Shanxi Province (202204010931026)
This publication has 35 references indexed in Scilit:
- Graph attention networks with adaptive neighbor graph aggregation for cold-start recommendationJournal of Intelligent Information Systems, 2024
- Higher-order embedded learning for heterogeneous information networks and adaptive POI recommendationInformation Processing & Management, 2024
- IGCN: Item Influence Enhanced Graph Convolution Networks for Recommendation of Cold-Start Items2023
- AdaML: An Adaptive Meta-Learning model based on user relevance for user cold-start recommendationKnowledge-Based Systems, 2023
- Information gain based dynamic support set construction for cold-start recommendationJournal of Intelligent Information Systems, 2023
- Relation-propagation meta-learning on an explicit preference graph for cold-start recommendationKnowledge-Based Systems, 2023
- A review on semi-supervised clusteringInformation Sciences, 2023
- MetaGC-MC: A graph-based meta-learning approach to cold-start recommendation with/without auxiliary informationInformation Sciences, 2022
- Approaches and algorithms to mitigate cold start problems in recommender systems: a systematic literature reviewJournal of Intelligent Information Systems, 2022
- Memory-augmented meta-learning on meta-path for fast adaptation cold-start recommendationConnection Science, 2021