Abstract: A method for AI-driven natural language search includes receiving a user query for one or more items from a user, processing the user query by searching against at least one relational database associated with the query, where the relational database is generated by extracting features from electronic documents of a plurality of items associated with the one or more items and by identifying specifications or respective values corresponding to the extracted features of the plurality of items, generating one or more query results based on the processing of the user query, where the one or more results include at least one item identified from the plurality of items and a justification for explaining an irrelevance of the at least one item, and transmitting the one or more query results to a user device for presentation to the user.

Abstract: Examples of the disclosure provide a system and method for obtaining one or more current candidate resumes and one or more past candidate resumes associated with a role and analyzing full text of the obtained resumes to identify one or more items. Weighted values are determined for the identified items using a dimension reduction technique, and a probability score is calculated for each of the obtained current candidate resumes based on selection data associated with the obtained past candidate resumes. A keyword score is calculated for each of the obtained current candidate resumes based on a presence of one or more keywords associated with the role in the obtained current candidate resumes. A final candidate score is generated for each of the obtained current candidate resumes based on the keyword score and the calculated probability score, and the generated final candidate scores are output in association with the obtained current candidate resumes to a user interface.

Abstract: Examples of the disclosure provide a system and method for obtaining one or more current candidate resumes and one or more past candidate resumes associated with a role and analyzing full text of the obtained resumes to identify one or more items. Weighted values are determined for the identified items using a dimension reduction technique, and a probability score is calculated for each of the obtained current candidate resumes based on selection data associated with the obtained past candidate resumes. A keyword score is calculated for each of the obtained current candidate resumes based on a presence of one or more keywords associated with the role in the obtained current candidate resumes. A final candidate score is generated for each of the obtained current candidate resumes based on the keyword score and the calculated probability score, and the generated final candidate scores are output in association with the obtained current candidate resumes to a user interface.