Abstract: A computing system generates recommendations for users within the context of a network service. To account for objectives of various users associated with the network service, some of which may not reach optimality at the same time, the computing system generates values associated with each of the objectives separately. For example, for each objective, the system may train a computer model to produce a representative value. To generate a recommendation of an entity for a user, the system uses the generated objective values as inputs to an optimization algorithm. The optimization step may use linear programming or quadratic programming to generate a recommendation score, for example. This two-step process allows the system to account for multiple objectives and makes the system easily adaptable to change when the set of objectives is updated.

Abstract: A computing system generates recommendations for users within the context of a network service. To account for objectives of various users associated with the network service, some of which may not reach optimality at the same time, the computing system generates values associated with each of the objectives separately. For example, for each objective, the system may train a computer model to produce a representative value. To generate a recommendation of an entity for a user, the system uses the generated objective values as inputs to an optimization algorithm. The optimization step may use linear programming or quadratic programming to generate a recommendation score, for example. This two-step process allows the system to account for multiple objectives and makes the system easily adaptable to change when the set of objectives is updated.

Abstract: A computing system generates recommendations for users within the context of a network service. To account for objectives of various users associated with the network service, some of which may not reach optimality at the same time, the computing system generates values associated with each of the objectives separately. For example, for each objective, the system may train a computer model to produce a representative value. To generate a recommendation of an entity for a user, the system uses the generated objective values as inputs to an optimization algorithm. The optimization step may use linear programming or quadratic programming to generate a recommendation score, for example. This two-step process allows the system to account for multiple objectives and makes the system easily adaptable to change when the set of objectives is updated.