@csdummi The algorithm uses observed behaviors like how long you watch a video, or if you opened the comment drawer, to determine the next videos in your feed
@dansup got it. Those are the inputs, the big question of course is how then it reaches a decision on what the next post will be.
Could these data points be used to derive a certain "interest" level in different accounts, topics, content (in the later two cases, how'd they be identified?) and then find more or less of that account/topic/content type to show next?
A "good" algorithm in this sense'd probably have to classify the content in it's database a whole lot more, to be able to suggest "similar" or "different" content.
@dansup got it. Those are the inputs, the big question of course is how then it reaches a decision on what the next post will be.
Could these data points be used to derive a certain "interest" level in different accounts, topics, content (in the later two cases, how'd they be identified?) and then find more or less of that account/topic/content type to show next?
Gregory's Server
@dansup got it. Those are the inputs, the big question of course is how then it reaches a decision on what the next post will be.
Could these data points be used to derive a certain "interest" level in different accounts, topics, content (in the later two cases, how'd they be identified?) and then find more or less of that account/topic/content type to show next?
A "good" algorithm in this sense'd probably have to classify the content in it's database a whole lot more, to be able to suggest "similar" or "different" content.
@dansup got it. Those are the inputs, the big question of course is how then it reaches a decision on what the next post will be.
Could these data points be used to derive a certain "interest" level in different accounts, topics, content (in the later two cases, how'd they be identified?) and then find more or less of that account/topic/content type to show next?