A personalized content recommendation provisioning method and system are described, according to various implementations. In an implementation, a user profile is established for each of multiple users within an electronic data environment (e.g., the Internet). The user profile may include information collected via a registration of the user and information identifying content consumed by the user based at least in part on information collected by an associated tracking cookie. The user profile may be used to generate a personalized grade associated with each of multiple candidate content recommendations in a high-quality candidate content recommendation pool, determine a content recommendation scope associated with a user in view of a personalized grade associated with each of the plurality of candidate content recommendations, and display the content recommendation scope to the user.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: establishing, by a processing device, a plurality of high-quality candidate content recommendation pools, wherein each of the plurality of high-quality candidate content recommendation pools comprises a plurality of candidate content recommendations; generating a general grade associated with each of the plurality of candidate content recommendations in a first high-quality candidate content recommendation pool; generating a personalized grade associated with each of the plurality of candidate recommendations in the first high-quality candidate content recommendation pool, wherein the personalized grade is generated in view of personal information associated with a first user; determining a recommendation scope associated with the first user in view of the personalized grade associated with each of the plurality of candidate content recommendations, wherein the content recommendation scope comprise one or more scoped content recommendations; and displaying the content recommendation scope to the first user. 2. The method of claim 1, wherein each of the plurality of high-quality candidate content recommendation pools is associated with a user trust measurement. 3. The method of claim 2, further comprising determining the user trust measurement in view of a user trust signal. 4. The method of claim 1, wherein the general grade associated with a first candidate content recommendation of the plurality of a candidate content recommendations is determined in view of an online click through rate (CTR) derived from aggregated user data associated with the first candidate content recommendation. 5. The method of claim 1, further comprising: registering the first user by collecting registration information associated with the first user; and generating a user profile associated with the first user, wherein the user profile comprises the registration information and user activity. 6. The method of claim 5, wherein the user activity comprises information identifying content consumed by the first user collected by an associated tracking cookie. 7. The method of claim 1, wherein a set of candidate content recommendations of the content recommendation scope are filtered in view of a respective personalized grade associated with each of the candidate content recommendations of the content recommendation scope. 8. A system comprising: a memory comprising instructions; and a processing device operatively coupled to the memory, the processing device to execute the instructions to: establish, by a processing device, a plurality of high-quality candidate content recommendation pools, wherein each of the plurality of high-quality candidate content recommendation pools comprises a plurality of candidate content recommendations; generate a general grade associated with each of the plurality of candidate content recommendations in a first high-quality candidate content recommendation pool; generate a personalized grade associated with each of the plurality of candidate content recommendations in the first high-quality candidate content recommendation pool, wherein the personalized grade is generated in view of personal information associated with a first user; determine a content recommendation scope associated with the first user in view of the personalized grade associated with each of the plurality of candidate content recommendations; and display, to the first user, the content recommendation scope to the first user. 9. The system of claim 8, wherein each of the plurality of high-quality candidate content recommendation pools is associated with a user trust measurement. 10. The system of claim 9, the processing device to determine the user trust measurement in view of a user trust signal. 11. The system of claim 8, wherein the general grade associated with a first candidate content recommendation of the plurality of a candidate content recommendations is determined in view of an online click through rate (CTR) derived from aggregated user data associated with the first candidate content recommendation. 12. The system of claim 8, the processing device to: register the first user by collecting registration information associated with the first user; and generate a user profile associated with the first user, wherein the user profile comprises the registration information and user activity. 13. The system of claim 12, wherein the user activity comprises information identifying content consumed by the first user collected by an associated tracking cookie. 14. The system of claim 8, wherein a set of candidate content recommendations of the content recommendation scope are filtered in view of a respective personalized grade associated with each of the candidate content recommendations of the content recommendation scope. 15. A non-transitory computer-readable storage medium comprising executable instructions that, when executed by a processing device, cause the processing device to: establish, by the processing device, a plurality of high-quality candidate content recommendation pools, wherein each of the plurality of high-quality candidate content recommendation pools comprises a plurality of candidate content recommendations; generate a general grade associated with each of the plurality of candidate content recommendations in a first high-quality candidate content recommendation pool; generate a personalized grade associated with each of the plurality of candidate content recommendations in the first high-quality candidate content recommendation pool, wherein the personalized grade is generated in view of personal information associated with a first user; determine a content recommendation scope associated with the first user in view of the personalized grade associated with each of the plurality of candidate content recommendations; and display, to the first user, the content recommendation scope to the first user. 16. The non-transitory computer-readable storage medium of claim 15, wherein each of the plurality of high-quality candidate content recommendation pools is associated with a user trust measurement. 17. The non-transitory computer-readable storage medium of claim 16, the processing device to determine the user trust measurement in view of a user trust signal. 18. The non-transitory computer-readable storage medium of claim 15, wherein the general grade associated with a first candidate content recommendation of the plurality of a candidate content recommendations is determined in view of an online click through rate (CTR) derived from aggregated user data associated with the first candidate content recommendation. 19. The non-transitory computer-readable storage medium of claim 15, the processing device to: register the first user by collecting registration information associated with the first user; and generate a user profile associated with the first user, wherein the user profile comprises the registration information and user activity comprises information identifying content consumed by the first user collected by an associated tracking cookie. 20. The non-transitory computer-readable storage medium of claim 15, wherein a set of filtered content recommendations of the content recommendation scope are filtered in view of a respective personalized grade associated with each of the candidate content recommendations of the content recommendation scope.