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.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: accessing member profile data and user behavior log data associated with a member of an online social networking service; generating, 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, 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; ranking the plurality of intents, based on the plurality of intent prioritization scores; selecting one or more of the highest ranked intents; and displaying to the member, via a user interface, the one or more of the highest ranked intents. 2. The method of claim 1, wherein each intent-specific machine learning model is trained by: accessing a set of feature data associated with each of a plurality of members of the online social networking service, each set of feature data indicating member profile data and user behavior log data associated with the corresponding member and a value indicating whether the corresponding member explicitly specified the relevant intent; and training, based on the feature data, the corresponding intent-specific machine learning model. 3. The method of claim 1, wherein the intent corresponds to finding a job. 4. The method of claim 1, wherein the intent corresponds to growing the member's network. 5. The method of claim 1, wherein the intent corresponds to hiring another member for a job. 6. The method of claim 1, further comprising: accessing recommended task information identifying a list of recommended tasks associated with the highest ranked intent; and displaying the list of recommended tasks via a user interface. 7. The method of claim 6, further comprising: identifying tasks previously performed by the member; removing the tasks previously performed by the member from the list of recommended tasks, to thereby generate a modified list of recommended tasks; and displaying the modified list of recommended tasks via a user interface. 8. The method of claim 1, further comprising: accessing success metric information identifying one or more success metrics associated with the highest ranked intent; identifying metric values associated with the identified success metrics; and displaying the metric values associated with the identified success metrics via a user interface. 9. A system comprising: a processor; and a memory device holding an instruction set executable on the processor to cause the system to perform operations comprising: accessing member profile data and user behavior log data associated with a member of an online social networking service; generating, 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, 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; ranking the plurality of intents, based on the plurality of intent prioritization scores; selecting one or more of the highest ranked intents; and displaying to the member, via a user interface, the one or more of the highest ranked intents. 10. The system of claim 9, wherein each intent-specific machine learning model is trained by: accessing a set of feature data associated with each of a plurality of members of the online social networking service, each set of feature data indicating member profile data and user behavior log data associated with the corresponding member and a value indicating whether the corresponding member explicitly specified the relevant intent; and training, based on the feature data, the corresponding intent-specific machine learning model. 11. The system of claim 9, wherein the intent corresponds to finding a job. 12. The system of claim 9, wherein the intent corresponds to growing the member's network. 13. The system of claim 9, wherein the intent corresponds to hiring another member for a job. 14. The system of claim 9, wherein the operations further comprise: accessing recommended task information identifying a list of recommended tasks associated with the highest ranked intent; and displaying the list of recommended tasks via a user interface. 15. The system of claim 14, wherein the operations further comprise: identifying tasks previously performed by the member; removing the tasks previously performed by the member from the list of recommended tasks, to thereby generate a modified list of recommended tasks; and displaying the modified list of recommended tasks via a user interface. 16. The system of claim 9, wherein the operations further comprise: accessing success metric information identifying one or more success metrics associated with the highest ranked intent; identifying metric values associated with the identified success metrics; and displaying the metric values associated with the identified success metrics via a user interface. 17. A non-transitory machine-readable storage medium comprising instructions that, when executed by one or more processors of a machine, cause the machine to perform operations comprising: accessing member profile data and user behavior log data associated with a member of an online social networking service; generating, 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, 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; ranking the plurality of intents, based on the plurality of intent prioritization scores; selecting one or more of the highest ranked intents; and displaying to the member, via a user interface, the one or more of the highest ranked intents. 18. The storage medium of claim 17, wherein each intent-specific machine learning model is trained by: accessing a set of feature data associated with each of a plurality of members of the online social networking service, each set of feature data indicating member profile data and user behavior log data associated with the corresponding member and a value indicating whether the corresponding member explicitly specified the relevant intent; and training, based on the feature data, the corresponding intent-specific machine learning model. 19. The storage medium of claim 17, wherein the operations further comprise: accessing recommended task information identifying a list of recommended tasks associated with the highest ranked intent; and displaying the list of recommended tasks via a user interface. 20. The storage medium of claim 17, wherein the operations further comprise: accessing success metric information identifying one or more success metrics associated with the highest ranked intent; identifying metric values associated with the identified success metrics; and displaying the metric values associated with the identified success metrics via a user interface.