Hello everyone,
I’m curious about how LinkedIn’s real-time trigger works for suggesting influencers to follow based on liking their posts(see snapshot attached). I’ve noticed that for non-influencers who are only two connections away, the trigger suggests connecting instead.
As a product manager, I’m interested in understanding the application of data science versus machine learning in this interaction. My assumption is that a rule-based trigger is likely used based on data science insights, as people who like a post are more likely to want to see more content from that user.
However, I’d love to hear from the group about whether and how this could be an outcome of machine learning. My understanding is that machine learning algorithms on LinkedIn or any data source work better with extracted data sets and are therefore not exactly real-time.
What are your thoughts?
