Utilize high performing trained machine learning models for information retrieval in a web store

A method comprising:

• accessing by a computer system associated with an online retailer, a store including multiple machine learning models that are each trained to generate a ranking of search results in response to search queries received at the online retailer;
• for each of a plurality of search queries input at the online retailer:
• applying, by the computer system, one of the machine learning models selected from the store to generate a ranking of search results that match the received search query,
• wherein each of the machine learning models in the store is used to rank search results for approximately equivalent numbers of the plurality of search queries input at the online retailer;
• returning the ranked search results for output to a user who input the received search query;
• and
• measuring a performance of the returned search results;
• receiving a subsequent search query from a subsequent user of the online retailer;
• responsive to receiving the subsequent search query, selecting, by the computer system, one of the machine learning models based on the measured performance of the returned search results;
• and
• providing search results ranked using the selected machine learning model for output to the subsequent user.

A system comprising:

• a trained machine learning model storage comprising a non-transitory computer readable storage medium, the trained machine learning model storage storing multiple machine learning models that are each trained to generate a ranking of search results in response to search queries received at an online retailer;
• a performance monitoring system including a processor configured to, for each of a plurality of search queries input at the online retailer:
• apply one of the machine learning models selected from the store to generate a ranking of search results that match the received search query;
• return the ranked search results for output;
• and
• measure a performance of the returned search results;
• a decision engine including a processor configured to select, for a given search query received at the online retailer, one of the machine learning models based on measured performance of previously returned search results;
• and
• a search platform including a processor configured to:
• process the search queries input at the online retailer to identify sets of products that match each of the search queries;
• apply for each of the processed search queries, a machine learning model selected by the decision engine to rank the identified set of products that match the processed search query;
• and
• provide the ranked set of products for output.