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 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;

for each phrase from the plurality phrases, obtaining a weight value, a weight value of a phrase from the plurality phrases calculated based on phrases present in the random sample of member profiles and a target category from the one or more member categories, 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;

storing the plurality of weighted phrases in a database;

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;

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 the obtaining of a weight value for each phrase from the plurality phrases comprises:

extracting the plurality of phrases from the random sample of member profiles; and

for each phrase from the plurality phrases, calculating a weight value.

3. The method of claim 1 , wherein the obtaining of a weight value for each phrase from the plurality phrases comprises:

providing the plurality of phrases from the random sample of member profiles to a further computer system; and

receiving, from the further computer system, the plurality of weighted phrases.

4. The method of claim 1 , wherein the generating the category score for the member profile comprises:

accessing behavior data of a member represented by the member profile, the behavior data reflecting activities of the member in the on-line social network system;

generating a propensity score for the member profile, based on the behavior data of the member; and

utilizing the propensity score in generating the category score for the member profile.

5. The method of claim 1 , wherein the generating the category score for the member profile comprises:

accessing a further member profile, the further member profile being connected with the member profile; and

utilizing a category score of the further member profile for generating the category score for the member profile.

6. The method of claim 5 , wherein the generating the category score for the member profile comprises:

accessing a category score of a further member profile, the further member profile being connected with the member profile; and

assigning the category score of the further member profile to the member profile.

7. The method of claim 5 , wherein the generating the category score for the member profile comprises:

accessing a category score of a further member profile, the further member profile being connected with the member profile; and

adjusting the category score of the member profile utilizing the category score of the further member profile.

8. The method of claim 1 , comprising storing, in a database, the category score of the member profile as associated with the member profile.

9. The method of claim 1 , comprising:

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

10. The method of claim 9 , comprising:

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

11. 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, for each phrase from the plurality phrases, using the at least one processor, a weight value, a weight value of a phrase from the plurality phrases calculated based on phrases present in the random sample of member profiles and a target category from the one or more member categories, 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;

a storing module, implemented using at least one processor, to store the plurality of weighted phrases in a database, using the at least one processor;

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; and

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, implemented using at least one processor, to:

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.

12. The system of claim 11 , wherein the weight value module is to:

extract the plurality of phrases from the random sample of member profiles; and

for each phrase from the plurality phrases, calculate a weight value.

13. The system of claim 11 , wherein the weight value module is to:

provide the plurality of phrases from the random sample of member profiles to a further computer system; and

receive, from the further computer system, the plurality of weighted phrases.

14. The system of claim 11 , wherein the category score module is to:

access behavior data of a member represented by the member profile, the behavior data reflecting activities of the member in the on-line social network system;

generate a propensity score for the member profile, based on the behavior data of the member; and

utilize the propensity score in generating the category score for the member profile.

15. The system of claim 11 , wherein the category score module is to:

access a further member profile, the further member profile being connected with the member profile; and

utilize a category score of the further member profile for generating the category score for the member profile.

16. The system of claim 15 , wherein the category score module is to:

access a category score of a further member profile, the further member profile being connected with the member profile; and

assign the category score of the further member profile to the member profile.

17. The system of claim 15 , wherein the category score module is to:

access a category score of a further member profile, the further member profile being connected with the member profile; and

adjust the category score of the member profile utilizing the category score of the further member profile.

18. The system of claim 11 , wherein the storing module is to store, in a database, the category score of the member profile as associated with the member profile.

19. The system of claim 11 , wherein the member segmentation module to:

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

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

selecting 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;

obtaining, for each phrase from the plurality phrases, a weight value, a weight value of a phrase from the plurality phrases calculated based on phrases present in the random sample of member profiles and a target category from the one or more member categories, 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;

storing the plurality of weighted phrases in a database;

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

determine, 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;

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.