Systems, methods, and non-transitory computer readable media can determine one or more user-related metrics relating to each page of a plurality of pages associated with an administrator based on a first machine learning model. One or more recommendations relating to each page of the plurality of pages can be determined based on a second machine learning model. One or more pages of the plurality of pages for which to display cards including page updates in a feed of the administrator can be determined, based on the determined user-related metrics and the determined recommendations.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: determining, by a computing device, one or more user-related metrics relating to each page of a plurality of pages associated with an administrator based on a first machine learning model; determining, by the computing device, one or more recommendations relating to each page of the plurality of pages based on a second machine learning model; and determining, by the computing device, one or more pages of the plurality of pages for which to display cards including page updates in a feed of the administrator, based on the determined user-related metrics and the determined recommendations. 2. The computer-implemented method of claim 1, further comprising displaying the cards for the one or more pages in a content item in the feed of the administrator. 3. The computer-implemented method of claim 2, wherein a card for a page of the one or more pages includes page updates associated with the page. 4. The computer-implemented method of claim 3, wherein the card for the page displays one or more of: page information relating to the page, user-related metrics relating to the page, or recommendations relating to the page. 5. The computer-implemented method of claim 4, further comprising obtaining feedback associated with one or more of: the displayed cards for pages, the displayed user-related metrics, or the displayed recommendations. 6. The computer-implemented method of claim 3, wherein the content item includes a card that provides a list including at least one page not included in the one or more pages and a number of unread items associated with the at least one page. 7. The computer-implemented method of claim 1, further comprising training the first machine learning model based on training data indicating user-related metrics selected by a plurality of administrators. 8. The computer-implemented method of claim 1, further comprising training the second machine learning model based on training data indicating recommendations selected by a plurality of administrators. 9. The computer-implemented method of claim 1, wherein the determining one or more pages of the plurality of pages for which to display cards including page updates in a feed of the administrator is based on a third machine learning model. 10. The computer-implemented method of claim 9, further comprising training the third machine learning model based on training data indicating cards for pages selected by a plurality of administrators. 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: determining one or more user-related metrics relating to each page of a plurality of pages associated with an administrator based on a first machine learning model; determining one or more recommendations relating to each page of the plurality of pages based on a second machine learning model; and determining one or more pages of the plurality of pages for which to display cards including page updates in a feed of the administrator, based on the determined user-related metrics and the determined recommendations. 12. The system of claim 11, wherein the instructions further cause the system to perform displaying the cards for the one or more pages in a content item in the feed of the administrator. 13. The system of claim 12, wherein a card for a page of the one or more pages includes page updates associated with the page. 14. The system of claim 13, wherein the card for the page displays one or more of: page information relating to the page, user-related metrics relating to the page, or recommendations relating to the page. 15. The system of claim 11, wherein the determining one or more pages of the plurality of pages for which to display cards including page updates in a feed of the administrator is based on a third machine learning model. 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: determining one or more user-related metrics relating to each page of a plurality of pages associated with an administrator based on a first machine learning model; determining one or more recommendations relating to each page of the plurality of pages based on a second machine learning model; and determining one or more pages of the plurality of pages for which to display cards including page updates in a feed of the administrator, based on the determined user-related metrics and the determined recommendations. 17. The non-transitory computer readable medium of claim 16, wherein the method further comprises displaying the cards for the one or more pages in a content item in the feed of the administrator. 18. The non-transitory computer readable medium of claim 17, wherein a card for a page of the one or more pages includes page updates associated with the page. 19. The non-transitory computer readable medium of claim 18, wherein the card for the page displays one or more of: page information relating to the page, user-related metrics relating to the page, or recommendations relating to the page. 20. The non-transitory computer readable medium of claim 16, wherein the determining one or more pages of the plurality of pages for which to display cards including page updates in a feed of the administrator is based on a third machine learning model.