Systems and methods for ranking ephemeral content item collections associated with a social networking system
Systems, methods, and non-transitory computer readable media can perform a first ranking to rank each ephemeral content item collection of a plurality of ephemeral content item collections based on a probability of a user selecting the ephemeral content item collection, each ephemeral content item collection of the plurality of ephemeral content item collections including one or more ephemeral content items. One or more ephemeral content item collections from the first ranking can be provided in an ephemeral content feed of the user. A selection by the user of an ephemeral content item collection provided in the ephemeral content feed can be received. A second ranking to rank each ephemeral content item collection of the ephemeral content item collections provided in the ephemeral content feed other than the selected ephemeral content item collection based on a probability of the user spending time on the ephemeral content item collection can be performed.
1. A computer-implemented method comprising:
performing, by a computing system, a first ranking to rank each ephemeral content item collection of a plurality of ephemeral content item collections based on a probability of a user selecting the ephemeral content item collection, wherein each ephemeral content item collection of the plurality of ephemeral content item collections includes one or more ephemeral content items;
providing, by the computing system, one or more ephemeral content item collections from the first ranking in an ephemeral content feed of the user;
receiving, by the computing system, a selection by the user of an ephemeral content item collection provided in the ephemeral content feed; and
performing, by the computing system, a second ranking to rank each ephemeral content item collection of the ephemeral content item collections provided in the ephemeral content feed other than the selected ephemeral content item collection based on a probability of the user spending time on the ephemeral content item collection.
2. The computer-implemented method of claim 1 , further comprising initiating playback of one or more ephemeral content items of the selected ephemeral content item collection in an immersive viewer in response to receiving the selection by the user.
3. The computer-implemented method of claim 2 , wherein playback in the immersive viewer of one or more ephemeral content items of the ephemeral content item collections provided in the ephemeral content feed other than the selected ephemeral content item collection is performed based on an order of the second ranking.
4. The computer-implemented method of claim 3 , wherein the providing of the one or more ephemeral content item collections from the first ranking increases a likelihood of the user selecting the ephemeral content item collections provided in the ephemeral content feed, and wherein the performing the playback of the one or more ephemeral content items of the ephemeral content item collections provided in the ephemeral content feed other than the selected ephemeral content item collection based on the order of the second ranking increases a likelihood of the user spending time on the ephemeral content item collections provided in the ephemeral content feed within the immersive viewer.
5. The computer-implemented method of claim 1 , further comprising:
training one or more machine learning models based on features relating to one or more of: ephemeral content item collection attributes, ephemeral content item attributes, or user attributes; and
applying the trained machine learning models to determine one or more scores for an ephemeral content item collection.
6. The computer-implemented method of claim 5 , wherein the training the one or more machine learning models includes:
training a first machine learning model to determine a score indicative of a probability of a particular user selecting an ephemeral content item collection; and
training a second machine learning model to determine a score indicative of a probability of a particular user spending time on an ephemeral content item collection.
7. The computer-implemented method of claim 6 , wherein the first ranking is performed based on scores of the plurality of ephemeral content item collections as determined by the first machine learning model, and wherein the second ranking is performed based on scores of the ephemeral content item collections provided in the ephemeral content feed other than the selected ephemeral content item collection as determined by the second machine learning model.
8. The computer-implemented method of claim 1 , wherein the ephemeral content item collections provided from the first ranking include one or more of: ephemeral content item collections from the first ranking having scores that satisfy a threshold value or a predetermined number of top ranked ephemeral content item collections from the first ranking.
9. The computer-implemented method of claim 1 , wherein each of one or more ephemeral content items included in an ephemeral content item collection of the plurality of ephemeral content item collections is accessible only for a predetermined time period.
10. The computer-implemented method of claim 9 , wherein an ephemeral content item collection of the plurality of ephemeral content item collections is accessible only when at least one of one or more ephemeral content items included in the ephemeral content item collection is accessible.
11. A system comprising:
at least one hardware processor; and
a memory storing instructions that, when executed by the at least one processor, cause the system to perform:
performing a first ranking to rank each ephemeral content item collection of a plurality of ephemeral content item collections based on a probability of a user selecting the ephemeral content item collection, wherein each ephemeral content item collection of the plurality of ephemeral content item collections includes one or more ephemeral content items;
providing one or more ephemeral content item collections from the first ranking in an ephemeral content feed of the user;
receiving a selection by the user of an ephemeral content item collection provided in the ephemeral content feed; and
performing a second ranking to rank each ephemeral content item collection of the ephemeral content item collections provided in the ephemeral content feed other than the selected ephemeral content item collection based on a probability of the user spending time on the ephemeral content item collection.
12. The system of claim 11 , wherein the instructions further cause the system to perform initiating playback of one or more ephemeral content items of the selected ephemeral content item collection in an immersive viewer in response to receiving the selection by the user.
13. The system of claim 12 , wherein playback in the immersive viewer of one or more ephemeral content items of the ephemeral content item collections provided in the ephemeral content feed other than the selected ephemeral content item collection is performed based on an order of the second ranking.
14. The system of claim 11 , wherein the instructions further cause the system to perform:
training one or more machine learning models based on features relating to one or more of: ephemeral content item collection attributes, ephemeral content item attributes, or user attributes; and
applying the trained machine learning models to determine one or more scores for an ephemeral content item collection.
15. The system of claim 14 , wherein the training the one or more machine learning models includes:
training a first machine learning model to determine a score indicative of a probability of a particular user selecting an ephemeral content item collection; and
training a second machine learning model to determine a score indicative of a probability of a particular user spending time on an ephemeral content item collection.
16. A non-transitory computer readable medium including instructions that, when executed by at least one hardware processor of a computing system, cause the computing system to perform a method comprising:
performing a first ranking to rank each ephemeral content item collection of a plurality of ephemeral content item collections based on a probability of a user selecting the ephemeral content item collection, wherein each ephemeral content item collection of the plurality of ephemeral content item collections includes one or more ephemeral content items;
providing one or more ephemeral content item collections from the first ranking in an ephemeral content feed of the user;
receiving a selection by the user of an ephemeral content item collection provided in the ephemeral content feed; and
performing a second ranking to rank each ephemeral content item collection of the ephemeral content item collections provided in the ephemeral content feed other than the selected ephemeral content item collection based on a probability of the user spending time on the ephemeral content item collection.
17. The non-transitory computer readable medium of claim 16 , wherein the method further comprises initiating playback of one or more ephemeral content items of the selected ephemeral content item collection in an immersive viewer in response to receiving the selection by the user.
18. The non-transitory computer readable medium of claim 17 , wherein playback in the immersive viewer of one or more ephemeral content items of the ephemeral content item collections provided in the ephemeral content feed other than the selected ephemeral content item collection is performed based on an order of the second ranking.
19. The non-transitory computer readable medium of claim 16 , wherein the method further comprises:
training one or more machine learning models based on features relating to one or more of: ephemeral content item collection attributes, ephemeral content item attributes, or user attributes; and
applying the trained machine learning models to determine one or more scores for an ephemeral content item collection.
20. The non-transitory computer readable medium of claim 19 , wherein the training the one or more machine learning models includes:
training a first machine learning model to determine a score indicative of a probability of a particular user selecting an ephemeral content item collection; and
training a second machine learning model to determine a score indicative of a probability of a particular user spending time on an ephemeral content item collection.