System and method for automatically retrieving relevant digital advertisements from multiple channels

A computer-implemented method, comprising:

• obtaining, from a computing device, a search request identifying a query and seeking items to be displayed on a webpage of a website to a user;
• searching, based on the query, a first database to retrieve a first set of sponsored items associated with the website, wherein the first set of sponsored items are retrieved and ranked based on an optimization of conversion rate;
• searching, based on the query, a second database to retrieve a second set of sponsored items associated with the website, wherein the second set of sponsored items are retrieved and ranked based on an optimization of click-through rate;
• computing, for each sponsored item in the first set and the second set, a relevance score representing a degree of relevancy between the sponsored item and the query;
• filtering the first set of sponsored items based on their relevance scores and at least one predetermined threshold, to generate a first filtered set of sponsored items;
• generating, for each of the first filtered set of sponsored items, a first ranking score representing a likelihood of conversion using a first machine learning model;
• selecting, from the first filtered set, up to a predetermined first number of sponsored items based on their respective first ranking scores, to generate a first selected set of sponsored items;
• filtering the second set of sponsored items based on their relevance scores and at least one predetermined threshold, to generate a second filtered set of sponsored items;
• generating, for each of the second filtered set of sponsored items, a second ranking score representing a likelihood of click using a second machine learning model;
• selecting, from the second filtered set, up to a predetermined second number of sponsored items based on their respective second ranking scores, to generate a second selected set of sponsored items;
• generating, based on the first selected set of sponsored items and the second selected set of sponsored items, a ranked list of recommended items based on an advertisement auction mechanism;
• and
• transmitting, to the computing device, the ranked list of recommended items in response to the search request.

A non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause at least one device to perform operations comprising:

• obtaining, from a computing device, a search request identifying a query and seeking items to be displayed on a webpage of a website to a user;
• searching, based on the query, a first database to retrieve a first set of sponsored items associated with the website, wherein the first set of sponsored items are retrieved and ranked based on an optimization of conversion rate;
• searching, based on the query, a second database to retrieve a second set of sponsored items associated with the website, wherein the second set of sponsored items are retrieved and ranked based on an optimization of click-through rate;
• computing, for each sponsored item in the first set and the second set, a relevance score representing a degree of relevancy between the sponsored item and the query;
• filtering the first set of sponsored items based on their relevance scores and at least one predetermined threshold, to generate a first filtered set of sponsored items;
• generating, for each of the first filtered set of sponsored items, a first ranking score representing a likelihood of conversion using a first machine learning model;
• selecting, from the first filtered set, up to a predetermined first number of sponsored items based on their respective first ranking scores, to generate a first selected set of sponsored items;
• filtering the second set of sponsored items based on their relevance scores and at least one predetermined threshold, to generate a second filtered set of sponsored items;
• generating, for each of the second filtered set of sponsored items, a second ranking score representing a likelihood of click using a second machine learning model;
• selecting, from the second filtered set, up to a predetermined second number of sponsored items based on their respective second ranking scores, to generate a second selected set of sponsored items;
• generating, based on the first selected set of sponsored items and the second selected set of sponsored items, a ranked list of recommended items based on an advertisement auction mechanism;
• and
• transmitting, to the computing device, the ranked list of recommended items in response to the search request.