Method and system for personalizing offers

A system for personalizing offers, comprising one or more processors and one or more non-transitory computer-readable memories coupled to the one or more processors and configured with instructions executable by the one or more processors to cause the system to perform operations comprising:

• collecting response data comprising one or more offers made to each of a plurality of users of a platform and one or more observed responses, wherein the one or more offers are selected from a group of offer choices;
• creating a training dataset comprising the collected response data and one or more features associated with each of the plurality of users;
• training a machine learning model using the training dataset, wherein the training comprises:
• creating a leaf node based on the training dataset;
• obtaining predicted responses based on the leaf node and the one or more features;
• and
• constructing a decision tree based on the one or more features and differences between the predicted responses and the one or more observed responses;
• obtaining a plurality of projected profits for the platform using the trained machine learning model, wherein each of the plurality of projected profits corresponds to making one of the group of offer choices to one of the plurality of users;
• constructing an objective function based on the plurality of projected profits comprising a plurality of decision variables;
• converting the objective function into a dual objective function;
• decomposing the dual objective function into a plurality of objectives corresponding to the plurality of users, each of the plurality of objectives comprising a subset of the plurality of decision variables;
• parallelly processing the plurality of objectives to determine values of each subset of the plurality of decision variables;
• determining the values of the plurality of decision variables based on the values of the each subset;
• and
• determining, based on the values of the plurality of decision variables, a combination of one or more offers from the group of offer choices for distributing to the plurality of users.

A non-transitory computer-readable storage medium configured with instructions executable by one or more processors to cause the one or more processors to perform operations comprising:

• collecting response data comprising one or more offers made to each of a plurality of users of a platform and one or more observed responses, wherein the one or more offers are selected from a group of offer choices;
• creating a training dataset comprising the collected response data and one or more features associated with each of the plurality of users;
• training a machine learning model using the training dataset, wherein the training comprises:
• creating a leaf node based on the training dataset;
• obtaining predicted responses based on the leaf node and the one or more features;
• and
• constructing a decision tree based on the one or more features and differences between the predicted responses and the one or more observed responses;
• obtaining a plurality of projected profits for the platform using the trained machine learning model, wherein each of the plurality of projected profits corresponds to making one of the group of offer choices to one of the plurality of users;
• constructing an objective function based on the plurality of projected profits comprising a plurality of decision variables;
• converting the objective function into a dual objective function;
• decomposing the dual objective function into a plurality of objectives corresponding to the plurality of users, each of the plurality of objectives comprising a subset of the plurality of decision variables;
• parallelly processing the plurality of objectives to determine values of each subset of the plurality of decision variables;
• determining the values of the plurality of decision variables based on the values of the each subset;
• and
• determining, based on the values of the plurality of decision variables, a combination of one or more offers from the group of offer choices for distributing to the plurality of users.