Method and system to determine a category score of a social network member

1. A method comprising:

using at least one processor coupled to a memory, selecting a seed sample of member profiles in an on-line social network system, a member profile from the member profiles representing a member of the on-line social network system, the on-line social network system maintaining one or more member categories, the seed sample of member profiles comprising a plurality of phrases, each member profile from the member profiles comprising two or more phrases;

associating each profile from the seed sample of member profiles with a target category from the one or more member categories;

for each phrase from the plurality phrases, obtaining a weight value utilizing regularized linear regression, a weight value of a phrase from the plurality phrases calculated based on presence or absence of a respective phrase in the seed sample of member profiles, a combination of a phrase from the plurality phrases and its weight value comprising a weighted phrase, the plurality of phrases with their respective weight values comprising a plurality of weighted phrases for the target category, the obtaining of a weight value for each phrase from the plurality phrases comprises identifying a neutral phrase from the plurality phrases and assigning a zero weight value to the neutral phrase;

accessing a member profile from the member profiles and the plurality of weighted phrases;

based on a presence of one or more phrases from the plurality of weighted phrases in the member profile, generating a category score for the member profile, the category score indicating a likelihood of the member profile being associated with the target category;

retrieving, from a database, the category score of the member profile;

comparing the category score of the member profile to a threshold value; and

based on a result of the comparing, selectively identifying the member profile as associated with the target category.

2. The method of claim 1 , wherein each profile from the seed sample of member profiles comprises data indicative of a purchased subscription to a service provided by the on-line social networking system.

3. The method of claim 1 , comprising assigning a negative weight value to a phrase from the plurality phrases based on the absence of the phrase in the seed sample of member profiles.

4. The method of claim 1 , comprising assigning a weight value to a phrase from the plurality phrases based on frequency of the phrase in the seed sample of member profiles.

5. The method of claim 1 , wherein the obtaining of a weight value for each phrase from the plurality phrases comprises utilizing support vector machine (SVM).

6. The method of claim 1 , wherein the obtaining of a weight value for each phrase from the plurality phrases comprises utilizing Naïve Bayes algorithm.

7. The method of claim 1 , comprising:

responsive to identifying the member profile as associated with the target category, sending a communication to a member represented by the member profile.

8. A computer-implemented system comprising:

a sample selector, implemented using at least one processor, to select, using the at least one processor, a random sample of member profiles in an on-line social network system, a member profile from the member profiles representing a member of the on-line social network system, the on-line social network system maintaining one or more member categories, the random sample of member profiles comprising a plurality of phrases, each member profile from the member profiles comprising two or more phrases;

a weight value module, implemented using at least one processor, to obtain, using regularized linear regression, for each phrase from the plurality phrases, a weight value of a phrase from the plurality phrases calculated based on presence or absence of a respective phrase in the seed sample of member profiles, a combination of a phrase from the plurality phrases and its weight value comprising a weighted phrase, the plurality of phrases with their respective weight values comprising a plurality of weighted phrases for the target category, the weight value identify a neutral phrase from the plurality phrases and assign a zero weight value to the neutral phrase;

an access module, implemented using at least one processor, to access a member profile from the member profiles and the plurality of weighted phrases, using the at least one processor;

a category score module, implemented using at least one processor, to determine, using the at least one processor, based on a presence of one or more phrases from the plurality of weighted phrases in the member profile, a category score for the member profile, the category score indicating a likelihood of the member profile being associated with the target category; and

a member segmentation module to:

retrieve, from a database, the category score of the member profile;

compare the category score of the member profile to a threshold value; and

based on a result of the comparing, selectively identify the member profile as associated with the target category.

9. The system of claim 8 , wherein each profile from the seed sample of member profiles comprises data indicative of a purchased subscription to a service provided by the on-line social networking system.

10. The system of claim 8 , wherein the weight value module is to assign a negative weight value to a phrase from the plurality phrases based on the absence of the phrase in the seed sample of member profiles.

11. The system of claim 8 , wherein the weight value module is to assign a weight value to a phrase from the plurality phrases based on frequency of the phrase in the seed sample of member profiles.

12. The system of claim 8 , wherein the weight value module is to utilize support vector machine (SVM).

13. The system of claim 8 , wherein the weight value module is to utilize Naïve Bayes algorithm.

14. A machine-readable non-transitory storage medium having instruction data to cause a machine to perform operations comprising:

selecting a seed sample of member profiles in an on-line social network system, a member profile from the member profiles representing a member of the on-line social network system, the on-line social network system maintaining one or more member categories, the seed sample of member profiles comprising a plurality of phrases, each member profile from the member profiles comprising two or more phrases;

for each phrase from the plurality phrases, obtaining a weight value utilizing regularized linear regression, a weight value of a phrase from the plurality phrases calculated based on presence or absence of a respective phrase in the seed sample of member profiles, a combination of a phrase from the plurality phrases and its weight value comprising a weighted phrase, the plurality of phrases with their respective weight values comprising a plurality of weighted phrases for the target category, the obtaining of a weight value for each phrase from the plurality phrases comprises identifying a neutral phrase from the plurality phrases and assigning a zero weight value to the neutral phrase;

accessing a member profile from the member profiles and the plurality of weighted phrases; and

determining, based on a presence of one or more phrases from the plurality of weighted phrases in the member profile, a category score for the member profile, the category score indicating a likelihood of the member profile being associated with the target category;

retrieving, from a database, the category score of the member profile;

comparing the category score of the member profile to a threshold value; and

based on a result of the comparing, selectively identifying the member profile as associated with the target category.