Identification of trending content using social network activity and user interests

1. A system comprising one or more physical computing devices, the system configured to at least:

obtain content request data regarding requests, initiated during a period of time, for a plurality of different content items, the requests generated by user computing devices and containing uniform resource locators (URLs) of requested content items;

modify the content request data at least partly by converting the URLs in at least some of the requests to canonical URLs, wherein converting a URL of a content item to a canonical URL comprises discarding URL text unnecessary for identifying the content item;

identify, using the modified content request data, a set of content items that have been requested at least a threshold number of times during the period of time;

generate scores for content items in the set of content items, wherein a score for a first content item of the set of content items is based at least partly on a difference between a current time and a time at which the first content item first reaches a threshold level of popularity;

select a subset of the content items from the set of content items based at least partly on the scores;

select a set of trending content items from the subset of content items based at least partly on information, obtained from one or more social network servers, reflecting an amount of social network activity associated with individual content items of the set of trending content items in the period of time; and

generate a list of user-specific trending content items for a user, the list of user-specific content items generated from the set of trending content items based at least partly on an analysis of content interests associated with the user and corresponding characteristics of content items in the list of user-specific trending content items.

2. The system of claim 1 , wherein individual content items comprise a content page, multimedia presentation, image, video, document, or network-accessible file.

3. The system of claim 1 , wherein the content interests associated with the user are determined based at least partly on an analysis, conducted using a machine learning model, of previous content requests initiated by the user.

4. The system of claim 1 , wherein the content interests associated with the user are determined based at least partly on historical shopping information associated with the user.

5. The system of claim 1 , wherein the system is further configured to at least:

generate a screenshot for a least one of the user-specific trending content items using a server-based content rendering module; and

provide the screenshot to a user device of the user in connection with the list of user-specific trending content items.

6. The system of claim 1 , wherein the information reflecting social network activity is distinct from the content request data.

7. The system of claim 1 , wherein the system processes the requests for content, and generates the content request data based on the requests, by operating as an intermediary between the user computing devices and content servers that host the content items.

8. The system of claim 1 , wherein the system is additionally configured to generate, for each of a plurality of the content items, a respective hash of a selected content portion of the content item, and to compare the hashes to identify different content items that contain the same content.

9. The system of claim 1 , wherein the system is configured to use an application program interface of a social network to retrieve the information reflecting the amount of social network activity.

10. A system comprising:

a data repository that stores a record of content requests made by user devices to content servers, the record of content requests including uniform resource locators (URLs) included in the content requests, the URLs specifying requested content items; and

a computing system comprising one or more computing devices, the computing system programmed to identify trending content items by a process that comprises:

generating a modified record of content requests at least partly by converting URLs in the record of content requests to canonical URLs, wherein converting a URL of a content item to a canonical URL comprises discarding URL text unnecessary for identifying the content item;

determining, based on the modified record of content requests, rates of change in popularity of particular content items;

selecting, based at least partly on the rates of change in popularity, a subset of the content items that satisfy a popularity condition;

retrieving, for individual content items in the subset, social network interaction data indicating levels to which the respective content items have been the subject of social network activity; and

selecting, from the subset of content items, based at least partly on the social network interaction data, particular content items that are trending.

11. The system of claim 10 , further comprising selecting, from the trending content items, particular content items to present to a user based on a combination of (1) content interests of the user, said content interests identified by analyzing content requested by the user, and (2) characteristics of the trending content items.

12. The system of claim 11 , wherein the system uses a machine learning model to learn the content interests of the user.

13. The system of claim 10 , wherein the system processes and content requests, and generates said record of the content requests, by operating as an intermediary between the user devices and content servers that host the content items.

14. The system of claim 10 , wherein the system, in generating the modified record of content requests, is additionally configured to generate, for each of a plurality of the content items, a respective hash of a selected content portion of the content item, and to compare the hashes to identify different content items that contain the same content.

15. The system of claim 10 , wherein the system is configured to retrieve the social network interaction data using an application program interface of a social network.

16. The system of claim 10 , wherein the social network interaction data for a content item is based at least partly on how many times the content item has been shared by social network users.

17. The system of claim 10 , wherein the social network interaction data for a content item is based at least partly on how many times the content item has been commented on by social network users.