Systems and methods for page recommendations based on page reciprocity
Systems, methods, and non-transitory computer readable media configured to determine whether a candidate content item may be presented in response to an indication of approval by a user regarding a seed content item according to a first technique. It is determined whether the seed content item may be presented in response to an indication of approval by the user regarding the candidate content item according to a second technique. Features, including a reciprocity feature based on the determining whether a candidate item may be presented and the determining whether the seed content item may be presented, are processed to generate a probability that the user will interact with the candidate content item.
1. A computer-implemented method comprising:
determining, by a computing system, whether a candidate content item may be presented in response to an indication of approval by a user regarding a seed content item according to a first technique, wherein the seed content item is a seed page and the candidate content item is a candidate page;
determining, by the computing system, whether the seed content item may be presented in response to an indication of approval by the user regarding the candidate content item according to a second technique; and
processing, by the computing system, features, including a reciprocity feature based on the determining whether a candidate content item may be presented and the determining whether the seed content item may be presented, to generate a probability that the user will interact with the candidate content item.
2. The computer-implemented method of claim 1 , further comprising:
determining an existence of reciprocity associated with the reciprocity feature based on the first technique providing a forward recommendation and the second technique providing a reverse recommendation.
3. The computer-implemented method of claim 1 , further comprising:
determining a value of the reciprocity feature.
4. The computer-implemented method of claim 3 , wherein the value of the reciprocity feature is binary.
5. The computer-implemented method of claim 3 , wherein the value of the reciprocity feature is a score based on a strength of reciprocity between the seed content item and the candidate content item.
6. The computer-implemented method of claim 1 , wherein the first technique and the second technique are same.
7. The computer-implemented method of claim 1 , wherein the first technique and the second technique are different.
8. The computer-implemented method of claim 1 , wherein the processing features further comprises:
applying a machine learning technique based on the features.
9. The computer-implemented method of claim 1 , further comprising:
selecting the candidate content item for presentation to the user based on the probability that the user will interact with the candidate content item.
10. A system comprising:
at least one processor; and
a memory storing instructions that, when executed by the at least one processor, cause the system to perform:
determining whether a candidate content item may be presented in response to an indication of approval by a user regarding a seed content item according to a first technique, wherein the seed content item is a seed page and the candidate content item is a candidate page;
determining whether the seed content item may be presented in response to an indication of approval by the user regarding the candidate content item according to a second technique; and
processing features, including a reciprocity feature based on the determining whether a candidate content item may be presented and the determining whether the seed content item may be presented, to generate a probability that the user will interact with the candidate content item.
11. The system of claim 10 , wherein the seed content item is a seed page and the candidate content item is a candidate page.
12. The system of claim 10 , further comprising:
determining an existence of reciprocity associated with the reciprocity feature based on the first technique providing a forward recommendation and the second technique providing a reverse recommendation.
13. The system of claim 10 , further comprising:
determining a value of the reciprocity feature.
14. The system of claim 10 , wherein the first technique and the second technique are same.
15. A non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform a method comprising:
determining whether a candidate content item may be presented in response to an indication of approval by a user regarding a seed content item according to a first technique, wherein the seed content item is a seed page and the candidate content item is a candidate page;
determining whether the seed content item may be presented in response to an indication of approval by the user regarding the candidate content item according to a second technique; and
processing features, including a reciprocity feature based on the determining whether a candidate content item may be presented and the determining whether the seed content item may be presented, to generate a probability that the user will interact with the candidate content item.
16. The non-transitory computer-readable storage medium of claim 15 , wherein the seed content item is a seed page and the candidate content item is a candidate page.
17. The non-transitory computer-readable storage medium of claim 15 , further comprising:
determining an existence of reciprocity associated with the reciprocity feature based on the first technique providing a forward recommendation and the second technique providing a reverse recommendation.
18. The non-transitory computer-readable storage medium of claim 15 , further comprising:
determining a value of the reciprocity feature.
19. The non-transitory computer-readable storage medium of claim 15 , wherein the first technique and the second technique are same.