System and method for generating recommendations with cold starts

A recommendation system, comprising:

• a hardware-based processor;
• a memory configured to store instructions, configured to provide the instructions to the hardware-based processor, and configured to store item taxonomy data for at least one item, to store client descriptive data for at least one client, and to store historical response data for at least one item responded to by the at least one client, wherein the item taxonomy data includes an item feature of an existing item and wherein the client descriptive data includes a client feature of an existing client;
• and
• a set of modules configured to implement the instructions provided to the hardware-based processor, the set of modules including:
• a machine learning module configured to generate a response probability matrix using the historical response data, the item taxonomy data, and the client descriptive data, wherein the response probability matrix includes a probability of a response of the at least one client, including a new client, to the at least one item, including a new item, and wherein the probability of the response in the response probability matrix in the case of the new client is generated using the machine learning module performing a first similarity comparison of the client feature of the existing client in the client descriptive data to a client feature of the new client;
• and
• a recommendation module configured to generate and output a recommendation corresponding to input data using the response probability matrix.

A recommendation system, comprising:

• an input device configured to receive input data;
• a hardware-based processor;
• a memory configured to store instructions, configured to provide the instructions to the hardware-based processor, and configured to store item taxonomy data for at least one item, to store client descriptive data for at least one client, and to store historical response data for at least one item responded to by the at least one client, wherein the item taxonomy data includes an item feature of an existing item and wherein the client descriptive data includes a client feature of an existing client;
• and
• a set of modules configured to implement the instructions provided to the hardware-based processor, the set of modules including:
• a trained machine learning module configured to generate a response probability matrix using the historical response data, the item taxonomy data, and the client descriptive data, wherein the response probability matrix includes a probability of a response of the at least one client, including a new client, to the at least one item, including a new item, and wherein the probability of the response in the response probability matrix in the case of the new client is generated using the trained machine learning module performing a first similarity comparison of the client feature of the existing client in the client descriptive data to a client feature of the new client;
• and
• a recommendation module configured to generate and output a recommendation corresponding to the input data using the response probability matrix.

A computer-based method, comprising:

• providing a hardware-based processor, a memory, and a set of modules, wherein the memory is configured to store instructions, configured to provide the instructions to the hardware-based processor, and configured to store item taxonomy data for at least one item, to store client descriptive data for at least one client, and to store historical response data for at least one item responded to by the at least one client, and the set of modules is configured to implement the instructions provided to the hardware-based processor, the set of modules including a trained machine learning module and a recommendation module;
• storing, in the memory, item taxonomy data for at least one item wherein the item taxonomy data includes an item feature of an existing item, client descriptive data for at least one client wherein the client descriptive data includes a client feature of an existing client, and historical response data for the at least one item responded to by the at least one client;
• generating a response probability matrix by the trained machine learning module using the historical response data, the item taxonomy data, and the client descriptive data, wherein the response probability matrix includes a probability of a response of the at least one client, including a new client, to the at least one item, including a new item, including:
• in the case of the new client, performing a first similarity comparison, using the trained machine learning module, of the client feature of the existing client in the client descriptive data to a client feature of the new client;
• and
• generating the probability of the response in the response probability matrix for the new client using the first similarity comparison;
• receiving a recommendation request including input data, wherein the input data includes client data or item data;
• generating, using the recommendation module, a recommendation corresponding to the input data using the response probability matrix;
• and
• outputting the recommendation from the recommendation module.