Hybrid deep-semantic matrix factorization for tag-aware personalized recommendation
Matrix factorization has now become a dominant solution for personalized recommendation on the Social Web. To alleviate the cold start problem, previous approaches have incorporated various additional sources of information into traditional matrix factorization models. These upgraded models, however...
Main Authors: | Xu, Z, Yuan, D, Lukasiewicz, T, Chen, C, Miao, Y, Xu, G |
---|---|
Formato: | Conference item |
Idioma: | English |
Publicado em: |
IEEE Digital Library
2020
|
Registos relacionados
-
Tag-Aware Personalized Recommendation Using a Hybrid Deep Model
Por: Xu, Z, et al.
Publicado em: (2017) -
Tag-Aware Personalized Recommendation Using a Deep-Semantic Similarity Model with Negative Sampling
Por: Xu, Z, et al.
Publicado em: (2016) -
Location-aware personalized news recommendation with deep semantic analysis
Por: Chen, C, et al.
Publicado em: (2017) -
Location-Aware News Recommendation Using Deep Localized Semantic Analysis
Por: Chen, C, et al.
Publicado em: (2017) -
Lightweight tag-aware personalized recommendation on the social web using ontological similarity
Por: Xu, Z, et al.
Publicado em: (2018)