Habit-based rewards systems and methods

A system, comprising:

• a memory for storing instructions for determining one or more incentives for a user;
• and
• a processor, communicatively coupled to the memory, configured to execute the instructions, the instructions causing the processor to:
• train a machine learning model using past purchases of a plurality of users stored on a repository as a training data set, wherein the machine learning model is trained, using the training data set including each of the past purchases of the plurality of users, to categorize, using a density-based clustering technique, the past purchases of the plurality of users into respective categories of purchases and to associate, using an association technique, the respective categories of purchases with a plurality of objectives, the plurality of objectives being based on votes received from other users having similar demographic information to s the user;
• receive a selection of an objective from among the plurality of objectives from the user via an application of a client device;
• identify, using the machine learning model, one or more incentives from among a plurality of incentives based on the selected objective and similarities between demographic information of the user and demographic information of other users from among the plurality of users;
• determine, using a regression algorithm of the machine learning model, a value for each of the one or more incentives being trained to determine the plurality of incentives based on the past purchases of the plurality of users using the density-based clustering technique;
• present the identified one or more incentives from among the plurality of incentives to the user, wherein the presented one or more incentives have a determined value that is inversely proportional to a popularity of the respective category based on purchase histories of the plurality of users;
• receive a selection of at least one of the one or more incentives from the user via the application of the client device;
• and
• determine whether to allocate the selected one or more incentives based on a performance of the user.

A non-transitory, tangible computer-readable device having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising:

• training a machine learning model using past purchases of a plurality of users stored on a repository as a training data set, wherein the machine learning model is trained, using the training data set including each of the past purchases of the plurality of users, to categorize, using a density-based clustering technique, the past purchases of the plurality of users into respective categories of purchases and to associate, using an association technique, the respective categories of purchases with a plurality of objectives, the plurality of objectives being based on votes received from other users having similar demographic information to a user;
• receiving a selection of an objective from among the plurality of objectives from the user via an application of a client device;
• identifying, using the machine learning model, one or more incentives from among a plurality of incentives based on the selected objective and similarities between demographic information of the user and demographic information of other users from among the plurality of users;
• determining, using a regression algorithm of the machine learning model, a value for each of the one or more incentives being trained to determine the plurality of incentives based on the past purchases of the plurality of users using the density-based clustering technique;
• presenting the identified one or more incentives from among the plurality of incentives to the user, wherein the presented one or more incentives have a value that is inversely proportional to a popularity of the respective category based on purchase histories of the plurality of users;
• receiving a selection of at least one of the one or more incentives from the user via the application of the client device;
• and
• determining whether to allocate the selected one or more incentives based on a performance of the user.