SYSTEMS AND METHODS FOR DETERMINING RECRUITING INTENT
First Claim
1. A computer-implemented method comprising:
- identifying a set of members of an online social network service that self-identify as recruiters;
clustering the set of members that self-identify as recruiters into a group of engaged recruiters and a second group of non-engaged recruiters;
categorizing the group of engaged recruiters as members exhibiting recruiting intent;
accessing behavioral log data associated with the members exhibiting recruiting intent, and classifying the behavioral log data as recruiting intent signature data; and
performing prediction modeling, by a machine including a memory and at least one processor, based on the recruiting intent signature data and a prediction model, to identify members of the online social network service that are associated with behavioral log data matching the recruiting intent signature data.
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Accused Products
Abstract
Techniques for identifying members of a social network service that exhibit recruiting intent are described. According to various embodiments, a set of members of an online social network service that self-identify as recruiters may be identified. The set of members that self-identify as recruiters may then be clustered into a group of engaged recruiters and a second group of non-engaged recruiters, and the group of engaged recruiters may be categorized as members exhibiting recruiting intent. Behavioral log data associated with the members exhibiting recruiting intent may then be accessed and classified as recruiting intent signature data. Thereafter, prediction modeling may be performed based on the recruiting intent signature data and a prediction model, to identify members of the online social network service that are associated with behavioral log data matching the recruiting intent signature data.
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Citations
20 Claims
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1. A computer-implemented method comprising:
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identifying a set of members of an online social network service that self-identify as recruiters; clustering the set of members that self-identify as recruiters into a group of engaged recruiters and a second group of non-engaged recruiters; categorizing the group of engaged recruiters as members exhibiting recruiting intent; accessing behavioral log data associated with the members exhibiting recruiting intent, and classifying the behavioral log data as recruiting intent signature data; and performing prediction modeling, by a machine including a memory and at least one processor, based on the recruiting intent signature data and a prediction model, to identify members of the online social network service that are associated with behavioral log data matching the recruiting intent signature data. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18)
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19. A system comprising:
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a machine including a memory and at least one processor; an identification module, executable by the machine, configured to; identify a set of members of an online social network service that self-identify as recruiters; cluster the set of members that self-identify as recruiters into a group of engaged recruiters and a second group of non-engaged recruiters; and categorize the group of engaged recruiters as members exhibiting recruiting intent; and a prediction module configured to; access behavioral log data associated with the members exhibiting recruiting intent, and classifying the behavioral log data as recruiting intent signature data; and perform prediction modeling based on the recruiting intent signature data and a prediction model, to identify members of the online social network service that are associated with behavioral log data matching the recruiting intent signature data.
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20. 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:
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identifying a set of members of an online social network service that self-identify as recruiters; clustering the set of members that self-identify as recruiters into a group of engaged recruiters and a second group of non-engaged recruiters; categorizing the group of engaged recruiters as members exhibiting recruiting intent; accessing behavioral log data associated with the members exhibiting recruiting intent, and classifying the behavioral log data as recruiting intent signature data; and performing prediction modeling based on the recruiting intent signature data and a prediction model, to identify members of the online social network service that are associated with behavioral log data matching the recruiting intent signature data.
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Specification