SYSTEMS AND METHODS FOR PROVIDING CONTENT
Systems, methods, and non-transitory computer-readable media can determine that a user is interacting with a software application running on a computing device. One or more content items to be prefetched for the software application are identified based on one or more machine learning models. A request to prefetch the one or more content items for the software application is generated.
1 . A computer-implemented method comprising:
training, by a computing system, a prefetch machine learning model based on training data received from a plurality of computing devices associated with a plurality of users,
wherein the training data includes user characteristics and labeling of previous occurrences of content interaction as examples,
wherein the user characteristics include content affinity scores determined based on an affinity machine learning model, a content affinity score being indicative of a user's predicted interest in a content item;
determining, by the computing system, that a first user is interacting with a software application running on a computing device;
identifying, by the computing system, one or more content items to be prefetched for the software application based on prefetch scores generated by the prefetch machine learning model, a prefetch score being indicative of a likelihood of the first user to view a content item; and
generating, by the computing system, a request to prefetch the one or more content items for the software application.
2 . The computer-implemented method of claim 1 , wherein the prefetch machine learning model is trained based on a set of labels.
3 . The computer-implemented method of claim 2 , wherein historical instances in which content was prefetched for a particular user and the particular user viewed the prefetched content are positive examples for training the prefetch machine learning model.
4 . The computer-implemented method of claim 2 , wherein historical instances in which content was prefetched for a particular user and the particular user did not view the prefetched content are negative examples for training the prefetch machine learning model.
5 . The computer-implemented method of claim 2 , wherein historical instances in which content was not prefetched for a particular user, and the particular user viewed the content are negative examples for training the prefetch machine learning model.
6 . The computer-implemented method of claim 1 , wherein the identifying the one or more content items to be prefetched further comprises
identifying, for each content item of the one or more content items, a portion of the content item to be prefetched based on the prefetch machine learning model.
7 . The computer-implemented method of claim 6 , wherein, for each content item of the one or more content items, the portion of the content item to be prefetched is determined based on historical user tendencies associated with the user.
8 . (canceled)
9 . The computer-implemented method of claim 1 , wherein the one or more content items to be prefetched are identified based on a determination that the one or more content items satisfy a prefetch score threshold.
10 . (canceled)
11 . 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 a method comprising:
training a prefetch machine learning model based on training data received from a plurality of computing devices associated with a plurality of users,
wherein the training data includes user characteristics and labeling of previous occurrences of content interaction as examples,
wherein the user characteristics include content affinity scores determined based on an affinity machine learning model, a content affinity score being indicative of a user's predicted interest in a content item;
determining that a first user is interacting with a software application running on a computing device;
identifying one or more content items to be prefetched for the software application based on prefetch scores generated by the prefetch machine learning model, a prefetch score being indicative of a likelihood of the first user to view a content item; and
generating a request to prefetch the one or more content items for the software application.
12 . The system of claim 11 , wherein the prefetch machine learning model is trained based on a set of labels.
13 . The system of claim 12 , wherein historical instances in which content was prefetched for a particular user and the particular user viewed the prefetched content are positive examples for training the prefetch machine learning model.
14 . The system of claim 12 , wherein historical instances in which content was prefetched for a particular user and the particular user did not view the prefetched content are negative examples for training the prefetch machine learning model.
15 . The system of claim 12 , wherein the minimum background time threshold wherein historical instances in which content was not prefetched for a particular user, and the particular user viewed the content are negative examples for training the prefetch machine learning model.
16 . 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:
training a prefetch machine learning model based on training data received from a plurality of computing devices associated with a plurality of users,
wherein the training data includes user characteristics and labeling of previous occurrences of content interaction as examples,
wherein the user characteristics include content affinity scores determined based on an affinity machine learning model, a content affinity score being indicative of a user's predicted interest in a content item;
determining that a first user is interacting with a software application running on a computing device;
identifying one or more content items to be prefetched for the software application based on prefetch scores generated by the prefetch machine learning model, a prefetch score being indicative of a likelihood of the first user to view a content item; and
generating a request to prefetch the one or more content items for the software application.
17 . The non-transitory computer-readable storage medium of claim 16 , wherein the prefetch machine learning model is trained based on a set of labels.
18 . The non-transitory computer-readable storage medium of claim 17 , wherein historical instances in which content was prefetched for a particular user and the particular user viewed the prefetched content are positive examples for training the prefetch machine learning model.
19 . The non-transitory computer-readable storage medium of claim 17 , wherein historical instances in which content was prefetched for a particular user and the particular user did not view the prefetched content are negative examples for training the prefetch machine learning model.
20 . The non-transitory computer-readable storage medium of claim 17 , wherein historical instances in which content was not prefetched for a particular user, and the particular user viewed the content are negative examples for training the prefetch machine learning model.
21 . The system of claim 11 , wherein the one or more content items to be prefetched are identified based on a determination that the one or more content items satisfy a prefetch score threshold.
22 . The non-transitory computer-readable storage medium of claim 16 , wherein the one or more content items to be prefetched are identified based on a determination that the one or more content items satisfy a prefetch score threshold.