Patent US10277715

Delivering content items using machine learning based prediction of user actions
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US10277715

1. A method comprising: (5) (5)

receiving, by an online system associated with a plurality of third party systems, information describing a search query specifying a search criteria, wherein the search criteria comprises an item type, wherein the search query requests items of the item type;

determining a match score indicative of the degree of match between the search criteria specified by the search query and items returned as search results in response to the search query;

storing in association with the user, the search query and the match score of the items returned as search results in response to the search query;

for each of one or more third party systems: receiving information describing one or more items of the item type,

determining a match score for each of the one or more items of the item type and the stored search query, and

determining whether any of the one or more items has a match score greater than the stored match score; and

responsive to identifying an item from a third party system, the item having a match score with the stored search query that is greater than the stored match score, sending, a content item associated with the identified item to a client device of the user.

2. The method of claim 1, wherein determining a match score indicative of the degree of match between the search criteria specified by the search query and items returned as search results in response to the search query comprises: (0) (3)

3. The method of claim 1, wherein determining a match score indicative of the degree of match between the search criteria specified by the search query and items returned as search results in response to the search query comprises: (0) (2)

4. The method of claim 1, wherein determining a match score indicative of the degree of match between the search criteria specified by the search query and items returned as search results in response to the search query comprises: (0) (2)

5. The method of claim 1, wherein receiving information describing one or more items of the item type comprises: (0) (3)

6. The method of claim 1, further comprising: (0) (2)

7. A method comprising: (6) (6)

receiving user information about a target user of an online system, the user information describing interactions performed by the target user on the online system;

receiving third party information from a third party system, the third party information describing an item associated with the third party that the target user is interested in acquiring;

retrieving, a trained machine learning model configured to receive as input the user information and the third party information describing the item and determine a likelihood that a user will perform an action associated with the item;

receiving item information indicating a change in a status of the item;

determining, using the trained machine learning model and the item information, a likelihood that the target user will perform an action associated with the item; and

providing, in response to the likelihood that the target user will perform the action associated with the item exceeding a threshold value, a content item associated with the item for display to the target user.

8. The method of claim 7, wherein the machine learning model is configured to generate a score indicative of a likelihood that the target user will perform an action associated with the item. (0)

9. The method of claim 7, wherein the machine learning model takes as input features describing actions performed by users connected to the target user on the online system. (0)

10. The method of claim 7, wherein the third party system includes a pixel in a user interface sent to a client device, the pixel associated with instructions for sending a request to a target online system, and wherein receiving third party information from the third party system comprises: (0) (1)

11. The method of claim 7, wherein the item is one of: a tangible object, an intangible service, or a message promoted by the third party. (0)

12. The method of claim 7, wherein the machine learning model is trained using user information of a plurality of other users, the other users and the target user associated with a same geographical region. (0)

13. The method of claim 7, wherein the machine learning model is trained using user information of a plurality of other users, the other users and the target user acquiring items using a same type of client device. (0)

14. A non-transitory computer readable medium storing executable computer program instructions, the computer program instructions comprising instructions that when executed cause a computer processor to: (6) (5)

receive, by an online system associated with a plurality of third party systems, information describing a search query specifying a search criteria, wherein the search criteria comprises an item type, wherein the search query requests items of the item type;

determine a match score indicative of the degree of match between the search criteria specified by the search query and items returned as search results in response to the search query;

store in association with the user, the search query and the match score of the items returned as search results in response to the search query;

for each of one or more third party systems: receive information describing one or more items of the item type,

determine a match score for each of the one or more items of the item type and the stored search query, and

determine whether any of the one or more items has a match score greater than the stored match score; and

responsive to identifying an item from a third party system, the item having a match score with the stored search query that is greater than the stored match score, send, a content item associated with the identified item to a client device of the user.

15. The computer readable medium of claim 14, wherein the computer program instructions for determining a match score indicative of the degree of match between the search criteria specified by the search query and items returned as search results in response to the search query comprise instructions that when executed cause the computer processor to: (0) (3)

16. The computer readable medium of claim 14, wherein the computer program instructions for determining a match score indicative of the degree of match between the search criteria specified by the search query and items returned as search results in response to the search query comprise instructions that when executed cause the computer processor to: (0) (2)

17. The computer readable medium of claim 14, wherein the computer program instructions for determining a match score indicative of the degree of match between the search criteria specified by the search query and items returned as search results in response to the search query comprise instructions that when executed cause the computer processor to: (0) (2)

18. The computer readable medium of claim 14, wherein the computer program instructions for receiving information describing one or more items of the item type comprise instructions that when executed cause the computer processor to: (0) (3)

19. The computer readable medium of claim 14, the computer program instructions further comprising instructions that when executed cause a computer processor to: (0) (2)

20. The computer readable medium of claim 14, wherein an item is one of: a tangible object, an intangible service, or a message promoted by the third party. (0)

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Patent US10277715

Delivering content items using machine learning based prediction of user actions

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