A system, a machine-readable storage medium storing instructions, and a computer-implemented method as described herein are directed to a Personalized Article Engine that generates respective prediction models for each article in a plurality of candidate articles in a social network system. The Personalized. Article Engine generates a respective article score according to each article's prediction model and at least one feature of a target member account. The Personalized Article Engine generates a plurality of output scores based on combining each respective article score with a corresponding article's global model score. The Personalized Article Engine ranks the output scores to identify a subset of candidate articles relevant to the target member account.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer system, comprising: a processor; a memory device holding at least one instruction set executable on the processor to cause the computer system to perform operations comprising: generating respective prediction models for each article in a plurality of candidate articles in a social network system; generating a respective article score according to each article's prediction model and at least one feature of a target member account; generating a plurality of output scores based on combining each respective article score with a corresponding article's global model score; and ranking the output scores to identify a subset of candidate articles relevant to the target member account. 2. The computer system as in claim 1, wherein generating respective prediction models for each article in a plurality of candidate articles in a social network systems comprises: training a first prediction model for a first article from the plurality of candidate articles in the social network system based on a plurality of features of each member account that has interacted with the first article in the social network system; and training a second prediction model for a second article from the plurality of candidate articles based on a plurality of features of each member account that has interacted with the second article in the social network system. 3. The computer system of claim 2, wherein a member account that has interacted with a respective candidate article comprises a member account that has performed any of the following member account actions in the social network service: access the respective candidate article, like the respective candidate article, post a comment on the respective candidate article, share the respective candidate article, and post the respective candidate article. 4. The computer system of claim 2. wherein each feature of a respective member account is based on at least any one of the following types of member account profile data: one or more industry descriptors, one or more job title descriptors, one or more employer company descriptors, one or more educational institution descriptors, one or more field of study descriptors, one or more geographic area descriptors and one or more professional level of experience indicators. 5. The computer system of claim 2, wherein generating a respective article score according to each article's prediction model and at least one feature of a target member account the first and the second prediction models comprises: wherein the first and the second prediction models are logistic regression models; and assembling a first vector according to a first set of encoded instructions representative of the first prediction model and each regression coefficient of the first prediction model; and assembling a second vector according to a second set of encoded instructions representative of the second prediction model and each regression coefficient of the second prediction model. 6. The computer system of claim 5, comprising: generating a first article score for the first article according to a plurality of features of the target member account and the first vector of the first prediction model; and generating a second article score for the second article according to the plurality of features of the target member account and the first vector of the first prediction model. 7. The computer system of claim 1, further comprising: wherein generating respective prediction models for each article in a plurality of candidate articles in a social network system comprises: storing, in a prediction model database, each respective prediction model for a given article in the plurality of candidate articles; wherein generating a respective article score comprises: for each respective article score: accessing in the prediction model database a respective prediction model; and accessing a member account database, wherein a portion of the member account database includes at least a portion of member account profile data of the target member account. 8. A non-transitory computer-readable medium storing executable instructions thereon, which, when executed by a processor, cause the processor to perform operations including: generating respective prediction models for each article in a plurality of candidate articles in a social network system; generating a respective article score according to each article's prediction model and at least one feature of a target member account; generating a plurality of output scores based on combining each respective article score with a corresponding article's global model score; and ranking the output scores to identify a subset of candidate articles relevant to the target member account. 9. The non-transitory computer-readable medium as in claim 8, wherein generating respective prediction models for each article in a plurality of candidate articles in a social network systems comprises: training a first prediction model for a first article from the plurality of candidate articles in the social network system based on a plurality of features of each member account that has interacted with the first article in the social network system; and training a second prediction model for a second article from the plurality of candidate articles based on a plurality of features of each member account that has interacted with the second article in the social network system. 10. The non-transitory computer-readable medium of claim 9, wherein a member account that has interacted with a respective candidate article comprises a member account that has performed any of the following member account actions in the social network service: access the respective candidate article, like the respective candidate article, post a comment on the respective candidate article, share the respective candidate article, and post the respective candidate article. 11. The non-transitory computer-readable medium of claim 9, wherein each feature of a respective member account is based on at least any one of the following types of member account profile data: one or more industry descriptors, one or more job title descriptors, one or more employer company descriptors, one or more educational institution descriptors, one or more field of study descriptors, one or more geographic area descriptors and one or more professional level of experience indicators. 12. The non-transitory computer-readable medium of claim 9, wherein generating a respective article score according to each article's prediction model and at least one feature of a target member account the first and the second prediction models comprises: wherein the first and the second prediction models are logistic regression models; and assembling a first vector according to a first set of encoded instructions representative of the first prediction model and each regression coefficient of the first prediction model; and assembling a second vector according to a second set of encoded instructions representative of the second prediction model and each regression coefficient of the second prediction model. 13. The non-transitory computer-readable medium of claim 12, comprising: generating a first article score for the first article according to a plurality of features of the target member account and the first vector of the first prediction model; and generating a second article score for the second article according to the plurality of features of the target member account and the first vector of the first prediction model. 14. The non-transitory computer-readable medium of claim 8, further comprising: wherein generating respective prediction models for each article in a plurality of candidate articles in a social network system comprises: storing, in a prediction model database, each respective prediction model for a given article in the plurality of candidate articles; wherein generating a respective article score comprises: for each respective article score: accessing in the prediction model database a respective prediction model; and accessing a member account database, wherein a portion of the member account database includes at least a portion of member account profile data of the target member account. 15. A method comprising: generating, via at least one hardware processor, respective prediction models for each article in a plurality of candidate articles in a social network system; generating a respective article score according to each article's prediction model and at least one feature of a target member account; generating a plurality of output scores based on combining each respective article score with a corresponding article's global model score; and ranking the output scores to identify a subset of candidate articles relevant to the target member account. 16. The method as in claim 15, wherein generating respective prediction models for each article in a plurality of candidate articles in a social network systems comprises: training a first prediction model for a first article from the plurality of candidate articles in the social network system based on a plurality of features of each member account that has interacted with the first article in the social network system; and training a second prediction model for a second article from the plurality of candidate articles based on a plurality of features of each member account that has interacted with the second article in the social network system. 17. The method of claim 16, wherein a member account that has interacted with a respective candidate article comprises a member account that has performed any of the following member account actions in the social network service: access the respective candidate article, like the respective candidate article, post a comment on the respective candidate article, share the respective candidate article, and post the respective candidate article. 18. The method of claim 16, wherein each feature of a respective member account is based on at least any one of the following types of member account profile data: one or more industry descriptors, one or more job title descriptors, one or more employer company descriptors, one or more educational institution descriptors, one or more field of study descriptors, one or more geographic area descriptors and one or more professional level of experience indicators. 19. The method of claim 16, wherein generating a respective article score according to each article's prediction model and at least one feature of a target member account the first and the second prediction models comprises: wherein the first and the second prediction models are logistic regression models; and assembling a first vector according to a first set of encoded instructions representative of the first prediction model and each regression coefficient of the first prediction model; and assembling a second vector according to a second set of encoded instructions representative of the second prediction model and each regression coefficient of the second prediction model. 20. The method of claim 19, comprising: generating a first article score for the first article according to a plurality of features of the target member account and the first vector of the first prediction model; and generating a second article score for the second article according to the plurality of features of the target member account and the first vector of the first prediction model.