Attribute selection for matchmaking

A system for assisting entities with improving the effectiveness of their profiles, the system comprising:

• one or more processors;
• and
• at least one memory coupled to the one or more processors and storing instructions that, when executed by the one or more processors, cause the system to perform operations including:
• storing profile data in a profile database of the system, the profile data including, for each respective entity of a plurality of entities associated with the system, a profile identifying attributes associated with the respective entity;
• obtaining, from the profile database using the one or more processors, a selection data vector including values each indicating a selection rate for a respective entity, each selection rate indicative of a rate at which users of the system have chosen to receive services from the respective entity after selecting the respective entity's profile;
• generating, for a selected entity associated with a user computing environment used to interact with the system, selection prediction data for the selected entity by analyzing, using an analysis model, the profile data associated with the selected entity, the selection prediction data predicting, for each respective change of a set of possible changes to the selected entity's profile, how the selection rate for the selected entity will change if the selected entity's profile is adjusted in accordance with the respective change, wherein the analysis model is trained, using a model training module in conjunction with a machine learning process, to predict how changes in a profile of a given entity will affect the selection rate for the given entity based on feeding the profile data associated with the given entity to the analysis model and comparing the values included in the selection data vector with selection rates included in a selection rate vector generated for the plurality of entities based on the attributes, and wherein the analysis model is retrained, using the model training module in conjunction with the machine learning process, until a difference between the values included in the selection data vector and the values included in the selection rate vector is below a threshold;
• selecting, from the selection prediction data, one or more recommended changes likely to result in an increase in the selection rate for the selected entity;
• and
• outputting, to the selected entity via an interface module communicatively coupled to the user computing environment, recommended profile adjustment data including a prompt recommending that the selected entity make at least one of the one or more recommended changes to the selected entity's profile.

A non-transitory computer-readable medium storing instructions that, when executed by at least one or more processors of a system for assisting entities with improving the effectiveness of their profiles, cause the system to perform operations including:

• storing profile data in a profile database of the system, the profile data including, for each respective entity of a plurality of entities associated with the system, a profile identifying attributes associated with the respective entity;
• obtaining, from the profile database using the one or more processors, a selection data vector including values each indicating a selection rate for a respective entity, each selection rate indicative of a rate at which users of the system have chosen to receive services from the respective entity after selecting the respective entity's profile;
• generating, for a selected entity associated with a user computing environment used to interact with the system, selection prediction data for the selected entity by analyzing, using an analysis model, the profile data associated with the selected entity, the selection prediction data predicting, for each respective change of a set of possible changes to the selected entity's profile, how the selection rate for the selected entity will change if the selected entity's profile is adjusted in accordance with the respective change, wherein the analysis model is trained, using a model training module in conjunction with a machine learning process, to predict how changes in a profile of a given entity will affect the selection rate for the given entity based on feeding the profile data associated with the given entity to the analysis model and comparing the values included in the selection data vector with selection rates included in a selection rate vector generated for the plurality of entities based on the attributes, and wherein the analysis model is retrained, using the model training module in conjunction with the machine learning process, until a difference between the values included in the selection data vector and the values included in the selection rate vector is below a threshold;
• selecting, from the selection prediction data, one or more recommended changes likely to result in an increase in the selection rate for the selected entity;
• and
• outputting, to the selected entity via an interface module communicatively coupled to the user computing environment, recommended profile adjustment data including a prompt recommending that the selected entity make at least one of the one or more recommended changes to the selected entity's profile.