Content filtering for slot optimization for sponsored searches using machine learning techniques

A computer-implemented method comprising:

• identifying intent features of a search query;
• using a first machine learning model to determine whether recommendations, mapped to a candidate item, qualify for one or more of first segment slots or second segment slots based on an extent of matching intent features of a search query entered via a user computer;
• using a second machine learning model to generate a relevance score for a query-item pair with standardized terms in both a query side based on the search query and an intent side based on the intent features of the search query;
• and
• populating the first segment slots and the second segment slots on a webpage, corresponding to the intent features of the search query, based on using the first machine learning model to determine whether the recommendations qualify for the one or more of the first segment slots or the second segment slots and based on using the second learning machine learning model to generate the relevance score for the query-item pair.

A non-transitory computer-readable medium storing computing instructions that, when executed on a processor, cause the processor to execute operations comprising:

• identifying intent features of a search query;
• using a first machine learning model to determine whether recommendations, mapped to a candidate item, qualify for one or more of first segment slots or second segment slots based on an extent of matching intent features of a search query entered via a user computer;
• using a second machine learning model to generate a relevance score for a query-item pair with standardized terms in both a query side based on the search query and an intent side based on the intent features of the search query;
• and
• populating the first segment slots and the second segment slots on a webpage, corresponding to the intent features of the search query, based on using the first machine learning model to determine whether the recommendations qualify for the one or more of the first segment slots or the second segment slots and based on using the second learning machine learning model to generate the relevance score for the query-item pair.