Cargando…
A multi-intent based multi-policy relay contrastive learning for sequential recommendation
Sequential recommendations have become a trending study for their ability to capture dynamic user preference. However, when dealing with sparse data, they still fall short of expectations. The recent contrastive learning (CL) has shown potential in mitigating the issue of data sparsity. Many item re...
Autor principal: | Di, Weiqiang |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9455276/ https://www.ncbi.nlm.nih.gov/pubmed/36091987 http://dx.doi.org/10.7717/peerj-cs.1088 |
Ejemplares similares
-
Attenuated and normalized item-item product network for sequential recommendation
por: Di, Weiqiang, et al.
Publicado: (2022) -
LightFIG: simplifying and powering feature interactions via graph for recommendation
por: Di, Weiqiang
Publicado: (2022) -
An evolutionary decomposition-based multi-objective feature selection for multi-label classification
por: Asilian Bidgoli, Azam, et al.
Publicado: (2020) -
Personalized movie recommendations based on deep representation learning
por: Li, Luyao, et al.
Publicado: (2023) -
Investigation of independent reinforcement learning algorithms in multi-agent environments
por: Lee, Ken Ming, et al.
Publicado: (2022)