A system, a machine-readable storage medium storing instructions, and a computer-implemented method as described herein are directed to a Fast Ranker Engine that identifies global model features present in an article in a social network service. The Fast Ranker Engine assembles respective fixed vectors based on at least one member account feature and each coefficient that corresponds to a present global article feature of the global model. The Fast Ranker Engine generates a transformation feature(s) for a prediction model of the article based on the respective fixed vectors.
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 the processor to cause the computer system to perform operations comprising: identifying global model features present in an article in a social network service; assembling respective fixed vectors based on at least one member account feature and each coefficient that corresponds to a present global article feature of the global model; and generating a transformation feature for a prediction model of the article based on the respective fixed vectors. 2. The computer system of claim 1, further comprising: storing in cache memory the global model and each prediction model of a respective article from a plurality of candidate articles, the global model including at least one global member feature having a corresponding global member feature coefficient and at least one global article feature having a corresponding global article feature coefficient, each prediction model including at least one per-article member feature having a corresponding per-article member coefficient. 3. The computer system of claim 2, wherein each global member feature 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. 4. The computer system of claim 3, wherein each global article feature is based on at least any one of the following types of social network data for one or more articles: one or more keywords descriptors, one or more author descriptors, one or more topic descriptors, an article age, presence in one or more member group discussions and an amount of article comments. 5. The computer system of claim 2, wherein identifying global model features present in an article in a social network service comprises: identifying presence, in a first article in a plurality of candidate articles, of a plurality of global article features from the global model; wherein assembling respective fixed vectors based on at least one member account feature and each coefficient that corresponds to a present global article feature of the global model comprises: assembling a fixed member feature vector for each respective global member feature; and assembling multiple instances of a fixed coefficient vector based on respective global article feature coefficients that correspond to the plurality of global article features present in the article. 6. The computer system as in claim 5, wherein generating a transformation feature for a prediction model of the article based on the respective fixed vectors comprises: for each global member feature, calculating feature transformation data for a respective global member feature and a given instance of the fixed coefficient vector. 7. The computer system as in claim 6, wherein calculating feature transformation data for a respective global member feature and a given instance of the fixed coefficient vector comprises: calculating first feature transformation data for a first global member feature and a first instance of the fixed coefficient vector; and calculating second feature transformation data for a second global member feature and a second instance of the fixed coefficient vector; 8. The computer system as in claim 7, further comprising: generating a first association between the first global member feature in the global model and the first feature transformation data; and generating a second association between the second global member feature in the global model and the second feature transformation data. 9. A non-transitory computer-readable medium storing executable instructions thereon, which, when executed by a processor, cause the processor to perform operations including: identifying global model features present in an article in a social network service; assembling respective fixed vectors based on at least one member account feature and each coefficient that corresponds to a present global article feature of the global model; and generating a transformation feature for a prediction model of the article based on the respective fixed vectors. 10. The non-transitory computer-readable medium of claim 9, further comprising: storing in cache memory the global model and each prediction model of a respective article from a plurality of candidate articles, the global model including at least one global member feature having a corresponding global member feature coefficient and at least one global article feature having a corresponding global article feature coefficient, each prediction model including at least one per-article member feature having a corresponding per-article member coefficient. 11. The non-transitory computer-readable medium of claim 10, wherein each global member feature 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 11, wherein each global article feature is based on at least any one of the following types of social network data for one or more articles: one or more keywords descriptors, one or more author descriptors, one or more topic descriptors, an article age, presence in one or more member group discussions and an amount of article comments. 13. The non-transitory computer-readable medium of claim 10, wherein identifying global model features present in an article in a social network service comprises: identifying presence, in a first article in a plurality of candidate articles, of a plurality of global article features from the global model; wherein assembling respective fixed vectors based on at least one member account feature and each coefficient that corresponds to a present global article feature of the global model comprises: assembling a fixed member feature vector for each respective global member feature; and assembling multiple instances of a fixed coefficient vector based on respective global article feature coefficients that correspond to the plurality of global article features present in the article; 14. The non-transitory computer-readable medium as in claim 13, wherein generating a transformation feature for a prediction model of the article based on the respective fixed vectors comprises: for each global member feature, calculating feature transformation data for a respective global member feature and a given instance of the fixed coefficient vector. 15. The non-transitory computer-readable medium as in claim 14, wherein calculating feature transformation data for a respective global member feature and a given instance of the fixed coefficient vector comprises: calculating first feature transformation data for a first global member feature and a first instance of the fixed coefficient vector; and calculating second feature transformation data for a second global member feature and a second instance of the fixed coefficient vector: 16. The non -transitory computer-readable medium as in claim 15, further comprising: generating a first association between the first global member feature in the global model and the first feature transformation data; and generating a second association between the second global member feature in the global model and the second feature transformation data. 17. A method comprising: identifying global model features present in an article in a social network service; assembling respective fixed vectors based on at least one member account feature and each coefficient that corresponds to a present global article feature of the global model; and generating a transformation feature for a prediction model of the article based on the respective fixed vectors. 18. The method of claim 17, further comprising: storing in cache memory the global model and each prediction model of a respective article from a plurality of candidate articles, the global model including at least one global member feature having a corresponding global member feature coefficient and at least one global article feature having a corresponding global article feature coefficient, each prediction model including at least one per-article member feature having a corresponding per-article member coefficient. 19. The method of claim 18, wherein identifying global model features present in an article in a social network service comprises: identifying presence, in a first article in a plurality of candidate articles, of a plurality of global article features from the global model; wherein assembling respective fixed vectors based on at least one member account feature and each coefficient that corresponds to a present global article feature of the global model comprises: assembling a fixed member feature vector for each respective global member feature; and assembling multiple instances of a fixed coefficient vector based on respective global article feature coefficients that correspond to the plurality of global article features present in the article; 20. The method as in claim 19, wherein generating a transformation feature for a prediction model of the article based on the respective fixed vectors comprises: for each global member feature, calculating feature transformation data for a respective global member feature and a given instance of the fixed coefficient vector.