User type affinity estimation using gamma-poisson model

A system comprising:

• one or more hardware processors;
• and
• a computer-readable medium coupled with the one or more hardware processors, the computer-readable medium comprising instructions stored thereon that are executable by the one or more hardware processors to cause the system to perform operations comprising:
• generating, for each of a plurality of users, a user click prediction table comprising a count corresponding to a number of times a user clicks on a type of media collection;
• analyzing the user click prediction table for each of the plurality of users using a machine learning model comprising a Poisson gamma model and trained to estimate an affinity a given user has for a type of media collection based on a feature vector comprising user features and features for each type of media collection and using a correction factor based on a user click history;
• storing prediction values generated by the machine learning model as user profile data for each user of the plurality of users, each prediction value indicating how much preference a given user has for a corresponding type of media collection;
• and
• sending one or more prediction values associated with a user identifier to a client device to cause display of a plurality of media collections ranked in an order based on the one or more predictions values.

A non-transitory computer-readable medium comprising instructions stored thereon that are executable by at least one processor to cause a computing device to perform operations comprising:

• generating, for each of a plurality of users, a user click prediction table comprising a count corresponding to a number of times a user clicks on a type of media collection;
• analyzing the user click prediction table for each of the plurality of users using a machine learning model comprising a Poisson gamma model and trained to estimate an affinity a given user has for a type of media collection based on a feature vector comprising user features and features for each type of media collection and using a correction factor based on a user click history;
• storing prediction values generated by the machine learning model as user profile data for each user of the plurality of users, each prediction value indicating how much preference a given user has for a corresponding type of media collection;
• and
• sending one or more prediction values associated with a user identifier to a client device to cause display of a plurality of media collections ranked in an order based on the one or more predictions values.