Systems and methods for machine learning models for search engine performance optimization

A method performed by a system for updating a machine learning model utilized in a search engine operation, the method comprising:

• identifying a set of search queries in stored search queries, wherein the set of search queries correspond to a request for a service offered by a service provider;
• applying the identified set of search queries to the search engine to generate one or more search results, wherein each search result has an assigned aggregate based on values of a set of parameters associated with the service offered by the service provider, wherein the set of parameters are based on configured objectives of the search engine identified using an optimization module;
• analyzing the values of the set of parameters to determine tradeoff point of each parameter, wherein the tradeoff point of a parameter occurs when updating the value of the parameter negatively impacts other parameters in the set of parameters;
• determining one or more weights to apply to the machine learning model based on the tradeoff points of the set of parameters;
• storing the determined one or more weights, wherein the stored weights are associated with the service corresponding to the identified set of stored search queries;
• and
• applying the determined one or more weights to the machine learning model for a search query corresponding to the service.

A search engine updating system comprising:

• one or more memory devices storing processor-executable instructions;
• and
• one or more processors configured to execute the instructions to cause the search engine updating system to perform:
• identifying a set of search queries in stored search queries, wherein the set of search queries correspond to a request for a service offered by a service provider;
• applying the identified set of search queries to the search engine to generate one or more search results for the service, wherein each search result has an assigned aggregate based on values of a set of parameters associated with the service offered by the service provider, wherein the set of parameters are based on configured objectives of the search engine identified using an optimization module;
• analyzing the values of the set of parameters to determine tradeoff point of each parameter, wherein the tradeoff point of a parameter occurs when updating the value of the parameter negatively impacts other parameters in the set of parameters;
• determining one or more weights to apply to the machine learning model based on the tradeoff points of the set of parameters;
• storing the determined one or more weights, wherein the stored weights are associated with the service corresponding to the identified set of stored search queries;
• and
• applying the determined one or more weights to the machine learning model for a search query corresponding to the service.