Presenting content to a social networking system user based on current relevance and future relevance of the content to the user

• US 10,303,727 B2
• Filed: 05/08/2015
• Issued: 05/28/2019
• Est. Priority Date: 05/08/2015
• Status: Active Grant

• Current Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Original Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Inventors: Marra, Gregory Matthew, Gavish, Yael
• Primary Examiner(s): Mofiz, Apu M
• Assistant Examiner(s): Holland, Sheryl L
• Application Number:
  US14/707,603
• Publication Number:
  US 20160328481A1
• Time in Patent Office: 1,481 Days
• CPC Class Codes: G06F 16/24575 using context
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06Q 50/00 Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism healthcare informatics G16H
• CPC Class Codes: H04L 67/306 User profiles

Abstract

A social networking system generates a feed of content items for presentation to a user based on measures of relevance of the content items to the user at a time when the feed is to be presented and at one or more future times. Content items are associated with value functions representing measures of relevance of the content items to the user over time. Based on the value functions for various content items, the social networking system determines an expected amount of user interaction with the feed when the feed is to be presented as well as predicted amounts of user interaction with the feed at one or more future times. Content items are selected for inclusion in the feed to maximize a combination of the expected amount of user interaction and the predicted amounts of user interaction with the feed.

Claims

1. A computer-implemented method comprising:

identifying current candidate content items available for presentation to a user of a social networking system via a feed of content items at a current time;

obtaining a value function for each current candidate content item, the value function for a candidate content item representing a measure of relevance of the candidate content item at different times;

for each of a plurality of combinations of the current candidate content items available for presentation to the user via the feed at the current time;

determining a predicted amount of user interaction with the combination of the current candidate content items at the current time by aggregating measures of relevance for the current candidate content items included in the combination of the current candidate content items based at least in part on the value functions for each of the current candidate content items included in the combination, and

for each of a plurality of additional times subsequent to the current time;

identifying a plurality of adjusted combinations of candidate content items available for presentation to the user via the feed at the additional time, each adjusted combination of candidate content items based at least in part on the combination of the current candidate content items, and

for each of the plurality of adjusted combinations of candidate content items available for presentation to the user via the feed at the additional time;

determining a predicted amount of user interaction with the adjusted combination of candidate content items by aggregating measures of relevance for the candidate content items included in the adjusted combination of candidate content items based at least in part on the value functions for each of the candidate content items included in the adjusted combination of candidate content items;

selecting a combination of the plurality of combinations of the current candidate content items for inclusion in the feed of content items at the current time, such that the selection maximizes an aggregate of the predicted amount of user interaction with the combination of the current content items at the current time and the predicted amounts of user interaction with each adjusted combination of candidate content items of each plurality of adjusted combinations of candidate content items at each of the plurality of additional times;

presenting, at the current time, the selected combination of the current candidate content items to the user via the feed of content items; and

for each of the plurality of additional times subsequent to the current time;

selecting an adjusted combination of the plurality of adjusted combinations of candidate content items for inclusion in the feed of content items at the additional time, such that the selection maximizes an aggregate of the predicted amount of user interaction with the adjusted combination of candidate content items at the additional time and the predicted amount of user interaction with each adjusted combination of candidate content items for each plurality of adjusted combinations of candidate content items at each additional time of the plurality of additional times that is subsequent to the additional time, by;

determining a frequency with which the user accesses the feed of content items provided by the social networking system is less than a threshold frequency;

responsive to the determining, identifying combinations of the current candidate content items from the plurality of combinations of the current candidate content items for inclusion in the feed of content items at the current time, the identified combinations including at least one current candidate content item having a value function with a measure of relevance less than a threshold within a time interval from the current time, the time interval associated with the threshold frequency; and

selecting a combination from the identified that maximizes the aggregate of the predicted amount of user interaction with the combination of the current content items at the current time and the predicted amounts of user interaction with each adjusted combination of candidate content items of each plurality of adjusted combinations of candidate content items at each of the plurality of additional times; and

presenting, at the additional time, the selected adjusted combination of candidate content items to the user via the feed of content items.

2. The computer-implemented method of claim 1, wherein the value function of a current candidate content item varies over time at a rate and the value function of candidate content item included in an adjusted combination of candidate content items that is not a current candidate content item varies over time at an additional rate that differs from the rate.

3. The computer-implemented method of claim 1, wherein obtaining the value function comprises retrieving the value function associated with a candidate content item, the value function specified by a user providing the candidate content item to the social networking system.

4. The computer-implemented method of claim 1, wherein obtaining the value function for each candidate content item comprises:

identifying characteristics of a candidate content item;

identifying prior interactions by the user with additional content items having at least a threshold number of characteristics matching the characteristics of the candidate content item; and

determining the value function of the candidate content item based at least in part on the identified prior interactions.

5. The computer-implemented method of claim 1, wherein the value function of a candidate content item is less than a threshold value at a time subsequent to the current time.

6. The computer-implemented method of claim 5, wherein the time subsequent to the current time at which the value function of the candidate content item is less than the threshold value is specified by a user providing the candidate content item to the social networking system.

7. The computer-implemented method of claim 5, wherein the time subsequent to the current time at which the value function of the candidate content item is less than the threshold value is determined by the social networking system based at least in part on characteristics of the candidate content item and prior interactions by the user with additional content items having at least a threshold number of characteristics matching the characteristics of the candidate content item.

8. The computer-implemented method of claim 1, wherein selecting the combination of the plurality of combinations of the current candidate content items for inclusion in the feed of content items at the current time, such that the selection maximizes the aggregate of the predicted amount of user interaction with the combination of the current content items at the current time and the predicted amounts of user interaction with each adjusted combination of candidate content items of each plurality of adjusted combinations of candidate content items at each of the plurality of additional times comprises:

associating weights with each predicted amount of user interaction with an adjusted combination of candidate content items, a weight associated with the adjusted combination of candidate content items based at least in part on a difference between the current time and an additional time associated with the adjusted combination of candidate content items;

applying the weights to the predicted amounts of user interaction with each adjusted combination of candidate content items of each plurality of adjusted combinations of candidate content items at each of the plurality of additional times;

for each of the plurality of combinations of the current candidate content items available for presentation to the user via the feed at the current time, generating an aggregation of predicted amount of user interaction with the combination of the current content items and the weighted predicted amounts of user interaction with each adjusted combination of candidate content items of each plurality of adjusted combinations of candidate content items at each of the plurality of additional times.

9. The computer-implemented method of claim 1, wherein selecting the combination of the plurality of combinations of the current candidate content items for inclusion in the feed of content items at the current time, such that the selection maximizes the aggregate of the predicted amount of user interaction with the combination of the current content items at the current time and the predicted amounts of user interaction with each adjusted combination of candidate content items of each plurality of adjusted combinations of candidate content items at each of the plurality of additional times comprises:

determining a likelihood of the user accessing the feed of content items between the current time and a time when a value function of a candidate content item has less than a threshold measure of relevance based at least in part on prior accesses of the feed of content items and times associated with the accesses of the feed of content items associated with the user by the social networking system;

responsive to the determined likelihood being less than a threshold likelihood, identifying combinations of the current candidate content items from the plurality of combinations of the current candidate content items for inclusion in the feed of content items at the current time, the identified combinations including the candidate content item; and

selecting a combination from the identified combinations that maximizes the aggregate of the predicted amount of user interaction with the combination of the current content items at the current time and the predicted amounts of user interaction with each adjusted combination of candidate content items of each plurality of adjusted combinations of candidate content items at each of the plurality of additional times.

10. 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 current candidate content items available for presentation to a user of a social networking system via a feed of content items at a current time;

obtain a value function for each current candidate content item, the value function for a candidate content item representing a measure of relevance of the candidate content item at different times;

for each of a plurality of combinations of the current candidate content items available for presentation to the user via the feed at the current time;

determine a predicted amount of user interaction with the combination of the current candidate content items at the current time by aggregating measures of relevance for the current candidate content items included in the combination of the current candidate content items based at least in part on the value functions for each of the current candidate content items included in the combination, and

for each of a plurality of additional times subsequent to the current time;

identify a plurality of adjusted combinations of candidate content items available for presentation to the user via the feed at the additional time, each adjusted combination of candidate content items based at least in part on the combination of the current candidate content items, and

for each of the plurality of adjusted combinations of candidate content items available for presentation to the user via the feed at the additional time;

determine a predicted amount of user interaction with the adjusted combination of candidate content items by aggregating measures of relevance for the candidate content items included in the adjusted combination of candidate content items based at least in part on the value functions for each of the candidate content items included in the adjusted combination of candidate content items;

select a combination of the plurality of combinations of the current candidate content items for inclusion in the feed of content items at the current time, such that the selection maximizes an aggregate of the predicted amount of user interaction with the combination of the current content items at the current time and the predicted amounts of user interaction with each adjusted combination of candidate content items of each plurality of adjusted combinations of candidate content items at each of the plurality of additional times;

present, at the current time, the selected combination of the current candidate content items to the user via the feed of content items; and

for each of the plurality of additional times subsequent to the current time;

select an adjusted combination of the plurality of adjusted combinations of candidate content items for inclusion in the feed of content items at the additional time, such that the selection maximizes an aggregate of the predicted amount of user interaction with the adjusted combination of candidate content items at the additional time and the predicted amount of user interaction with each adjusted combination of candidate content items for each plurality of adjusted combinations of candidate content items at each additional time of the plurality of additional times that is subsequent to the additional time, by;

determining a frequency with which the user accesses the feed of content items provided by the social networking system is less than a threshold frequency;

responsive to the determining, identifying combinations of the current candidate content items from the plurality of combinations of the current candidate content items for inclusion in the feed of content items at the current time, the identified combinations including at least one current candidate content item having a value function with a measure of relevance less than a threshold within a time interval from the current time, the time interval associated with the threshold frequency; and

selecting a combination from the identified that maximizes the aggregate of the predicted amount of user interaction with the combination of the current content items at the current time and the predicted amounts of user interaction with each adjusted combination of candidate content items of each plurality of adjusted combinations of candidate content items at each of the plurality of additional times; and

present, at the additional time, the selected adjusted combination of candidate content items to the user via the feed of content items.

11. The computer program product of claim 10, wherein the value function of a current candidate content item varies over time at a rate and the value function of a candidate content item included in an adjusted combination of candidate content items that is not a current candidate content item varies over time at an additional rate that differs from the rate.

12. The computer program product of claim 10, wherein obtaining the value function comprises retrieving the value function associated with a candidate content item, the value function specified by a user providing the candidate content item to the social networking system.

13. The computer program product of claim 10, wherein obtaining the value function for each candidate content item comprises:

identifying characteristics of a candidate content item;

identifying prior interactions by the user with additional content items having at least a threshold number of characteristics matching the characteristics of the candidate content item; and

determining the value function of the candidate content item based at least in part on the identified prior interactions.

14. The computer program product of claim 10, wherein the value function of a candidate content item is less than a threshold value at a time subsequent to the current time.

15. The computer program product of claim 14, wherein the time subsequent to the current time at which the value function of the candidate content item is less than the threshold value is specified by a user providing the candidate content item to the social networking system.

16. The computer program product of claim 14, wherein the time subsequent to the current time at which the value function of the candidate content item is less than the threshold value is determined by the social networking system based at least in part on characteristics of the candidate content item and prior interactions by the user with additional content items having at least a threshold number of characteristics matching the characteristics of the candidate content item.

17. The computer program product of claim 10, wherein select the combination of the plurality of combinations of the current candidate content items for inclusion in the feed of content items at the current time, such that the selection maximizes the aggregate of the predicted amount of user interaction with the combination of the current content items at the current time and the predicted amounts of user interaction with each adjusted combination of candidate content items of each plurality of adjusted combinations of candidate content items at each of the plurality of additional times comprises:

associate weights with each predicted amount of user interaction with an adjusted combination of candidate content items, a weight associated with the adjusted combination of candidate content items based at least in part on a difference between the current time and an additional time associated with the adjusted combination of candidate content items;

apply the weights to the predicted amounts of user interaction with the one or more additional feeds each adjusted combination of candidate content items of each plurality of adjusted combinations of candidate content items at each of the plurality of additional times;

for each of the plurality of combinations of the current candidate content items available for presentation to the user via the feed at the current time, generate an aggregation of a predicted amount of user interaction with the combination of the current content items and the weighted predicted amounts of user interaction with each adjusted combination of candidate content items of each plurality of adjusted combinations of candidate content items at each of the plurality of additional times.

18. The computer program product of claim 10, wherein select the combination of the plurality of combinations of the current candidate content items for inclusion in the feed of content items at the current time, such that the selection maximizes the aggregate of the predicted amount of user interaction with the combination of the current content items at the current time and the predicted amounts of user interaction with each adjusted combination of candidate content items of each plurality of adjusted combinations of candidate content items at each of the plurality of additional times comprises:

determine a likelihood of the user accessing the feed of content items between the current time and a time when a value function of a candidate content item has less than a threshold measure of relevance based at least in part on prior accesses of the feed of content items and times associated with the accesses of the feed of content items associated with the user by the social networking system;

responsive to the determined likelihood being less than a threshold likelihood, identify combinations of the current candidate content items from the plurality of combinations of the current candidate content items for inclusion in the feed of content items at the current time, the identified combinations including the candidate content item; and

select a combination from the identified combinations that maximizes the aggregate of the predicted amount of user interaction with the combination of the current content items at the current time and the predicted amounts of user interaction with each adjusted combination of candidate content items of each plurality of adjusted combinations of candidate content items at each of the plurality of additional times.