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What factors would you look at to come up with people you may know algorithm?

Data Scientist

Reddit

Viber

LinkedIn

Did you come across this question in an interview?

  • What factors would you look at to come up with people you may know algorithm?
  • Can you tell us which variables are most significant in determining potential recommended connections for a 'people you may know' feature?
  • What factors do you think should be prioritized when implementing a 'people you may know' program to optimize the suggested friend feature?
  • What are the primary data sets or inputs that would contribute to creating an effective algorithm for suggesting friends on social networks?
  • How would you consider social interactions, such as mutual connections, in developing an algorithm to suggest potential new contacts to users?
  • What criteria would best balance the importance of different information sources, such as role titles versus hobbies, in building an effective 'people you may know' algorithm?
  • What methods would you recommend to identify weak ties and facilitate building stronger connections in a 'people you may know' tool?
  • What kinds of machine learning or predictive algorithms could be applied to accurately recommend connections to users in a 'people you may know' feature?
  • What role would user feedback and interaction data play in optimizing and refining an algorithm that suggests friends in a 'people you may know' feature?
  • What are some key elements that would be considered in devising an accurate 'people you may know' algorithm?
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Interview question asked to Data Scientists interviewing at Reddit, LinkedIn and Viber: What factors would you look at to come up with people you may know algorithm?.