Abstract: In an embodiment, the disclosed technologies include receiving a set of digital inputs; where the set of digital inputs includes a candidate entity-member entity pair that includes candidate entity data and member entity data; where the member entity data has been extracted from a node of an online service; where an exact match has not been found between the candidate entity data and the member entity data; in response to the set of digital inputs, outputting models of the candidate entity data and the member entity data, respectively; where the models indicate weight values assigned to text in the candidate entity data and weight values assigned to text in the member entity data, respectively; calculating a similarity score using the models; in response to the similarity score matching a threshold, inputting the candidate entity-member entity pair to a classifier to produce a classification; where the classifier uses a machine learning model that has been trained using features derived from previously-analyz

Abstract: In an embodiment, the disclosed technologies include receiving a set of digital inputs; where the set of digital inputs includes a candidate entity-member entity pair that includes candidate entity data and member entity data; where the member entity data has been extracted from a node of an online service; where an exact match has not been found between the candidate entity data and the member entity data; in response to the set of digital inputs, outputting models of the candidate entity data and the member entity data, respectively; where the models indicate weight values assigned to text in the candidate entity data and weight values assigned to text in the member entity data, respectively; calculating a similarity score using the models; in response to the similarity score matching a threshold, inputting the candidate entity-member entity pair to a classifier to produce a classification; where the classifier uses a machine learning model that has been trained using features derived from previously-analyz

Abstract: Techniques for determining online content to provide to a member of an online social networking service based on their explicit and/or inferred intent are described. According to various embodiments, member profile data and user behavior log data associated with a member of an online social networking service is accessed. Based on the accessed data and a plurality of trained intent-specific machine learning models, a plurality of intent prioritization scores associated with a plurality of intents are generated, each intent prioritization score indicating an inferred likelihood that a member of the online social networking service is utilizing the online social networking service in connection with the corresponding intent. Thereafter, the plurality of intents are ranked, based on the plurality of intent prioritization scores, and one or more of the highest ranked intents are selected and displayed to the member.