Generalized additive machine-learned models for computerized predictions

A computerized method comprising:

• obtaining a first set of features derived from attributes of a first user in a social networking service:
• obtaining a second set of features derived from attributes of a first item in the social networking service,
• obtaining a third set of features derived from activity of the first user, with respect to a plurality of items, including the first item, in the social networking service;
• obtaining a fourth set of features derived from activity of a plurality of users, including the first user, with respect to the items in the social networking service;
• feeding the first and second sets of features into a machine-learned global model, producing a first computer-based numerical estimate of similarity between attributes of users and attributes of items;
• feeding the first and third sets of features into a machine-learned per-user model, producing a second computer-based numerical estimate of a likelihood that the first user will engage in an activity with an item in the social networking service;
• feeding the second and fourth sets of features into a machine-learned per-item model, producing a third computer-based numerical estimate of a likelihood that a user in the social networking service will engage in an activity with the first item;
• and
• combining the first, second, and third computer-based numerical estimates to produce an estimate of the likelihood that the first user will engage in an activity with the first item In the social networking service.

A non-transitory machine-readable storage medium comprising instructions which, when implemented by one or more machines, cause the one or more machines to perform operations comprising:

• obtaining a first set of features derived from attributes of a first user in a social networking service;
• obtaining a second set of features derived from attributes of a first item in the social networking service,
• obtaining a third set of features derived from activity of the first user, with respect to a plurality of items, including the first item, in the social networking service;
• obtaining a fourth set of features derived from activity of a plurality of users, including the first user, with respect to the items in the social networking service;
• feeding the first and second sets of features into a machine-learned global model, producing a first computer-based numerical estimate of similarity between attributes of users and attributes of items:
• feeding the first and third sets of features into a machine-learned per-user model, producing a second computer-based numerical estimate of a likelihood that the first user will engage in an activity with an item in the social networking service:
• feeding the second and fourth sets of features into a machine-learned per-item model, producing a third computer-based numerical estimate of a likelihood that a user in the social networking service will engage in an activity with the first item;
• and
• combining the first, second, and third computer-based numerical estimates to produce an estimate of the likelihood that the first user will engage in an activity with fee first hem in the social networking service.