Recommendation Network Using Machine Learning

A method comprising:

• collecting web activity data by monitoring web browsing activity of a first user, wherein the web activity data indicates preferences of the first user corresponding to a type of asset;
• determining social networking data that comprises a plurality of associations between a second plurality of users;
• identifying, based on purchase history data corresponding to the type of asset, one or more merchants where the second plurality of users purchased the one or more assets;
• inputting, to an input node of a plurality of nodes of a trained machine learning model, input data comprising:
• the web activity data,
• an indication of the one or more merchants,
• the social networking data, and
• the purchase history data,
• wherein the trained machine learning model comprises a machine learning model, implemented via an artificial neural network and comprising the plurality of nodes, trained, using training data, to:
• receive input indicating connections between a third plurality of users, second characteristics of the third plurality of users, and a plurality of assets purchased by the third plurality of users;
• and
• output, based on the input, one or more of the third plurality of users,
• wherein, during training of the trained machine learning model, a weight between at least two of the plurality of nodes was modified such that, when provided at least a portion of the training data, the machine learning model was trained out output one of a plurality of members based on connections between the plurality of members and the assets purchased by the plurality of members, and
• wherein the training data comprises connections between a first plurality of users that are members of a demographic of users, characteristics of the first plurality of users that are members of the demographic, indications of assets purchased by the first plurality of users, and a history of communications between the first plurality of users relating to the assets purchased by the first plurality of users;
• determining, based on output received via an output node of the plurality of nodes of the trained machine learning model, at least one second user of the second plurality of users, wherein the at least one second user has purchased a second asset that is the type of asset;
• generating, for the at least one second user, a notification prompting the second user to contact the first user regarding an intention of the first user to acquire the type of asset;
• and
• causing the notification to be transmitted to the second user.

One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause a computing device to:

• collect web activity data by monitoring web browsing activity of a first user, wherein the web activity data indicates preferences of the first user corresponding to a type of asset;
• determine social networking data that comprises a plurality of associations between a second plurality of users;
• identify, based on purchase history data corresponding to the type of asset, one or more merchants where the second plurality of users purchased the one or more assets;
• input, to an input node of a plurality of nodes of a trained machine learning model, input data comprising:
• the web activity data,
• an indication of the one or more merchants,
• the social networking data, and
• the purchase history data,
• wherein the trained machine learning model comprises a machine learning model, implemented via an artificial neural network and comprising the plurality of nodes, trained, using training data, to:
• receive input indicating connections between a third plurality of users, second characteristics of the third plurality of users, and a plurality of assets purchased by the third plurality of users;
• and
• output, based on the input, one or more of the third plurality of users,
• wherein, during training of the trained machine learning model, a weight between at least two of the plurality of nodes was modified such that, when provided at least a portion of the training data, the machine learning model was trained out output one of a plurality of members based on connections between the plurality of members and the assets purchased by the plurality of members, and
• wherein the training data comprises connections between a first plurality of users that are members of a demographic of users, characteristics of the first plurality of users that are members of the demographic, indications of assets purchased by the first plurality of users, and a history of communications between the first plurality of users relating to the assets purchased by the first plurality of users;
• determine, based on output received via an output node of the plurality of nodes of the trained machine learning model, at least one second user of the second plurality of users, wherein the at least one second user has purchased a second asset that is the type of asset;
• generate, for the at least one second user, a notification prompting the second user to contact the first user regarding an intention of the first user to acquire the type of asset;
• and
• cause the notification to be transmitted to the second user.