Abstract: Embodiments of the present disclosure provide methods, systems, and apparatuses for computing a channel incrementality ratio using a machine learning model.

Abstract: Embodiments of the present disclosure provide methods, systems, and apparatuses for programmatically determining and adjusting electronic bid values for a digital content object using a machine learning model.

Abstract: Embodiments of the present disclosure provide methods, systems, and apparatuses for programmatically determining and adjusting electronic bid values for a digital content object using a machine learning model.

Abstract: Embodiments of the present disclosure provide methods, systems, and apparatuses for computing a channel incrementality ratio using a machine learning model.

Abstract: Embodiments of the present disclosure provide methods, systems, and apparatuses for programmatically determining and adjusting electronic bid values for a digital content object based on different conversion rates during different network time periods.

Abstract: Embodiments of the present disclosure provide methods, systems, and apparatuses for computing a channel incrementality ratio using a machine learning model.

Abstract: Embodiments of the present disclosure provide methods, systems, and apparatuses for programmatically determining and adjusting electronic bid values for a digital content object based on different conversion rates during different network time periods.

Abstract: Embodiments of the present disclosure provide methods, systems, and apparatuses for computing a channel incrementality ratio using a machine learning model.

Abstract: Embodiments of the present disclosure provide methods, systems, and apparatuses for computing a channel incrementality ratio using a machine learning model.

Abstract: Embodiments of the present disclosure provide methods, systems, and apparatuses for computing a channel incrementality ratio using a machine learning model.

Abstract: Embodiments of the present disclosure provide methods, systems, and apparatuses for computing a channel incrementality ratio using a machine learning model.

Abstract: Systems, methods, and computer-readable storage media are provided for utilizing part-of-speech (POS) tagging of both the words included in a search query and the words included in potential search result documents to improve query alteration accuracy and search result ranking. Upon receiving a search query, POS tags are assigned to the words comprising the query to create query word-tag pairs. The query word-tag pairs are utilized to reformulate the query and are compared with document word-tag pairs included in a plurality of potential search result documents to determine a degree of similarity. The degree of similarity is utilized as an input in scoring and/or ranking the relevance of the potential search result documents with respect to one another.

Abstract: Systems, methods, and computer-readable storage media are provided for utilizing part-of-speech (POS) tagging of both the words included in a search query and the words included in potential search result documents to improve query alteration accuracy and search result ranking. Upon receiving a search query, POS tags are assigned to the words comprising the query to create query word-tag pairs. The query word-tag pairs are utilized to reformulate the query and are compared with document word-tag pairs included in a plurality of potential search result documents to determine a degree of similarity. The degree of similarity is utilized as an input in scoring and/or ranking the relevance of the potential search result documents with respect to one another.