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[logseq-plugin-git:commit] 2025-12-27T19:07:25.270Z

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ddiruscio
2025-12-27 20:08:10 +01:00
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pages/Omnivore___24-09-2023___Data science for next-generation recommender systems.md
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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]]
- * ### Highlights
- 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)