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Chen et al. [2] proposed to learn user preferences from tweet content, user social relations, and other explicit features like publisher authority based on collaborative ranking, in order to improve the personalized recommendation performance.
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NeighborGoods is based on collaborative consumption, a movement that combines sharing and renting, with social media often fuellingthe exchange.
In order to address this data sparsity problem, we propose three algorithms2 based on collaborative filtering.
This approach is based on collaborative algorithms for minimizing consumption of sensor energy.
Plista is a user-centric real-time personalization and recommendation network based on collaborative filtering.
This teamwork is based on collaborative discussion and decision-making among the team members.
Thematic analysis was used for all interview transcripts, an ongoing process based on collaborative discussion.
Level of evidence was assessed by two researchers independently based on collaboratives' plans of action.
We also introduce an aggregate function for the generation of author ranking based on publication ranking.
Based on risk ranking, control measures are developed.
This problem is more serious in collaborative ranking domain, in which calculating the users' similarities and recommending items are based on ranking data.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com