- full-title:: [Data science for next-generation recommender systems](https://omnivore.app/me/s-41060-023-00404-w-18ac7cbb212)
  site:: [link.springer.com](https://link.springer.com/content/pdf/10.1007/s41060-023-00404-w.pdf)
  author:: Shoujin Wang 
  labels:: [[ResearchPaper]] [[P1]] [[recsys]] 
  date-saved:: [[24-09-2023]]
  source:: [[Omnivore]]
  state:: [[Reading]]
	- collapsed:: true
	  * ### Highlights
	  collapsed:: true
		- > Data science has been the foundation of recommender systems for a long time [⤴️](https://omnivore.app/me/s-41060-023-00404-w-18ac7cbb212#cfd7078e-db00-44f3-b27e-8f2af0122401)
		- > various recommender systems have been developed using different data science and machine learning methodologies and techniques [⤴️](https://omnivore.app/me/s-41060-023-00404-w-18ac7cbb212#c0f64226-c49b-4b65-a681-290585565139)
		- > relationships between data science and recommender systems [⤴️](https://omnivore.app/me/s-41060-023-00404-w-18ac7cbb212#0adf6ccd-aa33-472e-8682-64a2cf693a60)