PREDICTING A USER QUALITY RATING FOR A CONTENT ITEM ELIGIBLE TO BE PRESENTED TO A VIEWING USER OF AN ONLINE SYSTEM

• US 20180082331A1
• Filed: 09/22/2016
• Published: 03/22/2018
• Est. Priority Date: 09/22/2016
• Status: Abandoned Application

• Current Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Original Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Inventors: Feldman, Matthew, Li, Shuo, Morris, Cassidy Jake, Mooser, Jonathan, Wang, Xu
• Application Number:
  US15/272,764
• Publication Number:
  US 20180082331A1
• Time in Patent Office: Days
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 30/0273 Determination of fees for advertising

Abstract

An online system selects content items for presentation to viewing users of the online system based on a composite score associated with each content item that includes a quality component and a revenue component. The revenue component is based on a monetary amount an advertiser associated with the content item is willing to pay for each interaction with the content item by a prospective viewing user, while the quality component indicates the quality of the content item to the prospective viewing user. The quality component is predicted based on explicit user quality ratings received from viewing users for various content items previously presented to the viewing users, in which the viewing users have at least a threshold measure of similarity to the prospective viewing user and/or the various content items rated by the viewing users have at least a threshold measure of similarity to the content item being scored.

Claims

1. A method comprising:

identifying an opportunity to present a content item to a prospective viewing user of an online system, the prospective viewing user associated with one or more user attributes;

determining a revenue score associated with the content item based at least in part on a value that an advertiser is willing to pay in exchange for each of a set of interactions with the content item received from the prospective viewing user;

retrieving a plurality of user quality ratings associated with one or more content items previously presented to a plurality of viewing users of the online system, each of the plurality of user quality ratings describing a quality of each of the one or more content items determined by a viewing user of the plurality of viewing users;

predicting a quality score indicative of a quality of the content item to the prospective viewing user, the quality score based at least in part on one or more of the plurality of user quality ratings determined by one or more of the plurality of viewing users associated with one or more additional user attributes having at least a threshold measure of similarity to the one or more user attributes associated with the prospective viewing user; and

determining a composite score associated with the content item based at least in part on the revenue score and the quality score.

2. The method of claim 1, wherein each of the one or more content items previously presented to the plurality of viewing users of the online system is associated with one or more content item features having at least a threshold measure of similarity to one or more additional content item features associated with the content item.

3. The method of claim 1, wherein one or more of the plurality of user quality ratings associated with the one or more content items comprise one or more results of a survey communicated to the plurality of viewing users.

4. The method of claim 1, wherein one or more of the plurality of user quality ratings associated with the one or more content items comprise crowdsourced user quality ratings.

5. The method of claim 1, wherein predicting the quality score indicative of a quality of the content item to the prospective viewing user comprises:

associating a weight with one or more of the plurality of user quality ratings associated with the one or more content items; and

predicting the user quality rating of the prospective viewing user indicating the quality of the content item based at least in part on the weight associated with one or more of the plurality of user quality ratings.

6. The method of claim 5, wherein the weight associated with one or more of the plurality of user quality ratings is based at least in part on one or more selected from a group consisting of:

a measure of similarity between the one or more user attributes associated with the prospective viewing user and the one or more additional user attributes associated with the one or more of the plurality of viewing users, a measure of similarity between one or more content item features associated with each of the one or more content items previously presented to the plurality of viewing users of the online system and one or more additional content item features associated with the content item, and any combination thereof.

7. The method of claim 1, wherein the quality score associated with the content item is further based at least in part on a predicted likelihood that the prospective viewing user will perform each of the set of interactions with the content item.

8. The method of claim 7, wherein the predicted likelihood that the prospective viewing user will perform each of the set of interactions with the content item is associated with a weight.

9. The method of claim 1, wherein the set of interactions with the content item is selected from a group consisting of:

clicking on the content item, expressing a preference for the content item, sharing the content item with additional users of the online system, commenting on the content item, attending an event associated with the content item, joining a group associated with the content item, subscribing to a service associated with the content item, purchasing a product associated with the content item, and any combination thereof.

10. The method of claim 1, wherein one or more of the plurality of user quality ratings associated with the one or more content items comprise relative user quality ratings associated with the one or more content items.

11. The method of claim 1, further comprising:

training a machine-learned model to predict the quality score indicative of the quality of the content item to the prospective viewing user based at least in part on the plurality of user quality ratings.

12. The method of claim 11, wherein the quality score indicative of the quality of the content item to the prospective viewing user is predicted using the machine-learned model.

13. The method of claim 1, further comprising:

receiving the plurality of user quality ratings associated with the one or more content items previously presented to the plurality of viewing users of the online system; and

storing the plurality of user quality ratings associated with the one or more content items.

14. The method of claim 1, further comprising:

ranking the content item among one or more additional content items based at least in part on the composite score associated with the content item; and

selecting a set of content items associated with at least a threshold ranking or at least a threshold composite score for presentation to the prospective viewing user.

15. The method of claim 14, further comprising:

presenting the set of content items associated with at least the threshold ranking or at least the threshold composite score to the prospective viewing user.

16. A computer program product comprising a computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to:

identify an opportunity to present a content item to a prospective viewing user of an online system, the prospective viewing user associated with one or more user attributes;

determine a revenue score associated with the content item based at least in part on a value that an advertiser is willing to pay in exchange for each of a set of interactions with the content item received from the prospective viewing user;

retrieve a plurality of user quality ratings associated with one or more content items previously presented to a plurality of viewing users of the online system, each of the plurality of user quality ratings describing a quality of each of the one or more content items determined by a viewing user of the plurality of viewing users;

predict a quality score indicative of a quality of the content item to the prospective viewing user, the quality score based at least in part on one or more of the plurality of user quality ratings determined by one or more of the plurality of viewing users associated with one or more additional user attributes having at least a threshold measure of similarity to the one or more user attributes associated with the prospective viewing user; and

determine a composite score associated with the content item based at least in part on the revenue score and the quality score.

17. The computer program product of claim 16, wherein each of the one or more content items previously presented to the plurality of viewing users of the online system is associated with one or more content item features having at least a threshold measure of similarity to one or more additional content item features associated with the content item.

18. The computer program product of claim 16, wherein one or more of the plurality of user quality ratings associated with the one or more content items comprise one or more results of a survey communicated to the plurality of viewing users.

19. The computer program product of claim 16, wherein one or more of the plurality of user quality ratings associated with the one or more content items comprise crowdsourced user quality ratings.

20. The computer program product of claim 16, wherein predict the quality score indicative of a quality of the content item to the prospective viewing user comprises:

associate a weight with one or more of the plurality of user quality ratings associated with the one or more content items; and

predict the user quality rating of the prospective viewing user indicating the quality of the content item based at least in part on the weight associated with one or more of the plurality of user quality ratings.

21. The computer program product of claim 20, wherein the weight associated with one or more of the plurality of user quality ratings is based at least in part on one or more selected from a group consisting of:

a measure of similarity between the one or more user attributes associated with the prospective viewing user and the one or more additional user attributes associated with the one or more of the plurality of viewing users, a measure of similarity between one or more content item features associated with each of the one or more content items previously presented to the plurality of viewing users of the online system and one or more additional content item features associated with the content item, and any combination thereof.

22. The computer program product of claim 16, wherein the quality score associated with the content item is further based at least in part on a predicted likelihood that the prospective viewing user will perform each of the set of interactions with the content item.

23. The computer program product of claim 22, wherein the predicted likelihood that the prospective viewing user will perform each of the set of interactions with the content item is associated with a weight.

24. The computer program product of claim 16, wherein the set of interactions with the content item is selected from a group consisting of:

clicking on the content item, expressing a preference for the content item, sharing the content item with additional users of the online system, commenting on the content item, attending an event associated with the content item, joining a group associated with the content item, subscribing to a service associated with the content item, purchasing a product associated with the content item, and any combination thereof.

25. The computer program product of claim 16, wherein one or more of the plurality of user quality ratings associated with the one or more content items comprise relative user quality ratings associated with the one or more content items.

26. The computer program product of claim 16, wherein the computer readable storage medium further has instructions encoded thereon that, when executed by the processor, cause the processor to:

train a machine-learned model to predict the quality score indicative of a quality of the content item to the prospective viewing user based at least in part on the plurality of user quality ratings.

27. The computer program product of claim 26, wherein the quality score indicative of the quality of the content item to the prospective viewing user is predicted using the machine-learned model.

28. The computer program product of claim 16, wherein the computer readable storage medium further has instructions encoded thereon that, when executed by the processor, cause the processor to:

receive the plurality of user quality ratings associated with the one or more content items previously presented to the plurality of viewing users of the online system; and

store the plurality of user quality ratings associated with the one or more content items.

29. The computer program product of claim 16, wherein the computer readable storage medium further has instructions encoded thereon that, when executed by the processor, cause the processor to:

rank the content item among one or more additional content items based at least in part on the composite score associated with the content item; and

select a set of content items associated with at least a threshold ranking or at least a threshold composite score for presentation to the prospective viewing user.

30. The computer program product of claim 29, wherein the computer readable storage medium further has instructions encoded thereon that, when executed by the processor, cause the processor to:

present the set of content items associated with at least the threshold ranking or at least the threshold composite score to the prospective viewing user.

31. A method comprising:

identifying an opportunity to present a content item to a prospective viewing user of an online system, the prospective viewing user associated with one or more user attributes;

predicting a quality score indicative of a quality of the content item to the prospective viewing user based at least in part on a plurality of user quality ratings associated with one or more content items previously presented to a plurality of viewing users of the online system, each of the plurality of viewing users associated with one or more additional user attributes having at least a threshold measure of similarity to the one or more user attributes associated with the prospective viewing user, each of the plurality of user quality ratings describing a quality of each of the one or more content items determined by a viewing user of the plurality of viewing users;

determining a composite score associated with the content item based at least in part on a value that an advertiser is willing to pay in exchange for each of a set of interactions with the content item received from the prospective viewing user and the quality score;

selecting a set of content items for presentation to the prospective viewing user based at least in part on the composite score associated with the content item; and

presenting the set of content items to the prospective viewing user.