A Social Recommendation Based on Metric Learning and Users’ Co-Occurrence Pattern
For personalized recommender systems, matrix factorization and its variants have become mainstream in collaborative filtering. However, the dot product in matrix factorization does not satisfy the triangle inequality and therefore fails to capture fine-grained information. Metric learning-based mode...
Guardado en:
Autores principales: | Xin Zhang, Jiwei Qin, Jiong Zheng |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
MDPI AG
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/e291a1667f834340a44ae68e6a51c3f6 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Exploring an Efficient POI Recommendation Model Based on User Characteristics and Spatial-Temporal Factors
por: Chonghuan Xu, et al.
Publicado: (2021) -
Measuring Product Similarity with Hesitant Fuzzy Set for Recommendation
por: Chunsheng Cui, et al.
Publicado: (2021) -
Functional Inequalities for Metric-Preserving Functions with Respect to Intrinsic Metrics of Hyperbolic Type
por: Marcelina Mocanu
Publicado: (2021) -
One Recommender Fits All? An Exploration of User Satisfaction With Text-Based News Recommender Systems
por: Mareike Wieland, et al.
Publicado: (2021) -
Contractive Mappings on Metric Spaces with Graphs
por: Simeon Reich, et al.
Publicado: (2021)