Determining popular and trending content characteristics

• US 9,898,748 B1
• Filed: 08/30/2012
• Issued: 02/20/2018
• Est. Priority Date: 08/30/2012
• Status: Expired due to Fees

• Current Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Original Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Inventors: Taylor, Brett R., Killian, David A., Hill, Peter F., Vaswani, Ameet N., Sham, Ivan King Yu, Atreya, Ranganath, Young, Samuel J.
• Primary Examiner(s): Chea, Philip
• Assistant Examiner(s): Ahmed, Mohammed
• Application Number:
  US13/599,870
• Time in Patent Office: 2,000 Days
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities

Abstract

Features are disclosed for analyzing requests for network accessible content, including but not limited to web pages, to determine which topics and other characteristics are popular or are gaining in popularity (“trending”). Content items or sources may be profiled to determine characteristics that two or more content items or sources may have in common. Content requests from multiple client devices may be tracked and analyzed to determine the trending or popular characteristics. Data feeds or reports regarding the summarized content requests may be generated and distributed to content servers, advertisers, and other entities. The data feeds may be used to tailor content, such as by highlighting or featuring content associated with the most-requested content characteristics, or utilizing demographic data to tailor content for different users.

Claims

1. A system for identifying trending content topics, the system comprising:

an intermediary system comprising one or more processors and a computer-readable memory, the intermediary system configured to operate as an intermediary between client devices and content sites; and

executable instructions stored in the computer-readable memory, wherein the one or more processors are programmed by the executable instructions to at least;

process a plurality of content requests from a plurality of the client devices, wherein each content request of the plurality of content requests corresponds to a content item of a plurality of content items, and wherein at least two content items of the plurality of content items are hosted by different content sites, said processing comprising retrieving the content items from the content sites and providing the content items to the client devices;

associate one or more topics, of a plurality of topics, with each content item of the plurality of content items based at least in part on a keyword analysis of the content item, wherein each topic is represented by a respective tag in a mapping table that maps Uniform Resource Locators of content items to corresponding tags;

generate count values for specific topics of the plurality of topics based on (1) the content requests and (2) the topics associated with corresponding content items, each count value corresponding to a respective topic and representing a measure of how often content items associated with the respective topic are requested, wherein generating the count values comprises maintaining at least first and second count values for a topic, the first count value corresponding to a first geographic region and representing a count of requests made by users in the first geographic region, the second count value corresponding to a second geographic region and representing a count of requests made by users in the second geographic region; and

generate, based on the count values, topic trend data reflecting topics associated with an increasing volume of content requests, wherein generating the topic trend data comprises using the count values to determine geographic-region-specific rates of change in popularity of particular topics.

2. The system of claim 1, wherein the one or more processors are further programmed to:

obtain demographic data regarding a user of a first client device of the plurality of client devices, wherein request data regarding a request received from the first client device is associated with the demographic data, and wherein the topic trend data further reflects demographic data regarding one or more users of the plurality of client devices.

3. The system of claim 1, wherein the one or more processors are further programmed to:

obtain device data regarding one or more characteristics of a first client device of the plurality of client devices, wherein request data regarding a request received from the first client device is associated with the device data, and wherein the topic trend data further reflects the device data regarding one or more client devices.

4. The system of claim 1, wherein the one or more processors are further programmed to transmit the topic trend data to a content provider as a data feed.

5. The system of claim 1, wherein the one or more processors are further programmed to provide the topic trend data to a content provider in response to a request from the content provider.

6. An intermediary system comprising one or more computing devices, said intermediary system programmed via executable program instructions to implement a process that comprises:

receiving requests from user devices for content pages hosted by content servers;

retrieving the requested content pages from the content servers on behalf of the user devices, and delivering the retrieved content pages to the user devices in fulfillment of said requests;

determining, based on an analysis of content of the retrieved content pages, topics associated with the retrieved content pages, wherein each topic is represented by a respective tag in a mapping table that maps Uniform Resource Locators of content pages to corresponding tags; and

generating topic trend data based at least partly on a volume of the received requests for content pages and the determined topics associated with the retrieved content pages, said topic trend data indicating trends associated with particular topics, wherein generating the topic trend data comprises maintaining, for at least a first topic, topic-specific count values representing a measure of how often content pages associated with the first topic are requested over respective periods of time, and using the topic-specific count values to determine a rate of change in popularity of the first topic;

wherein generating the topic-specific count values comprises maintaining at least first and second count values for a topic, the first count value corresponding to a first geographic region and representing a count of requests made by users in the first geographic region, the second count value corresponding to a second geographic region and representing a count of requests made by users in the second geographic region;

wherein generating the topic trend data comprises using the count values to determine geographic-region-specific rates of change in popularity of particular topics.

7. The intermediary system of claim 6, further comprising providing said topic trend data to at least one content provider as a data feed.

8. The intermediary system of claim 6, wherein generating the topic trend data comprises using device data regarding the user devices associated with particular page requests to determine device-type-specific topical trends.

9. The intermediary system of claim 6, wherein generating the topic trend data comprises using demographic data associated with at least some of the user devices to determine demographic-category-specific topical trends.

10. The intermediary system of claim 6, wherein the topic trend data comprises an identifier of a content page associated with a topic.

11. The intermediary system of claim 6, wherein the process further comprises generating a mapping table that maps URLs to topics based on the analysis of the retrieved pages, and wherein generating the topic trend data comprises using the mapping table to look up topics associated with specific URLs.

12. The intermediary system of claim 6, wherein generating the topic trend data comprises determining, for the first topic, a rate of change in requests for content pages corresponding to the first topic.

13. The intermediary system of claim 6, wherein determining the topics associated with the retrieved content pages comprises assigning a textual tag to each of a plurality of retrieved content pages based on keyword analyses of the retrieved content pages, the textual tag representing a particular topic.

14. A computer-implemented process, comprising:

assigning a topic to each of a plurality of content items, including content items of different respective content sites, based at least partly on keyword analyses of the content items, wherein each topic is represented by a respective tag in a mapping table that maps Uniform Resource Locators of content items to corresponding tags;

by an intermediary system that serves as an intermediary between user devices and content sites, processing requests from the user devices for the content items by retrieving the content items from the content sites and providing the content items to the user devices;

generating trend data for the topic based on the requests from the user devices for the content items, wherein generating the trend data comprises maintaining, for the topic and for each of a plurality of periods of time, a count value representing a count of said requests received over the respective period of time, and using the count values to measure a rate of change of popularity of the topic;

wherein generating the trend data further comprises maintaining at least first and second count values for the topic, the first count value corresponding to a first geographic region and representing a count of requests made by users in the first geographic region, the second count value corresponding to a second geographic region and representing a count of requests made by users in the second geographic region, and using the first and second count values to determine geographic-region-specific rates of change in popularity of the topic;

said process performed by execution of program code by one or more processors.

15. The method of claim 14, wherein the process further comprises generating the mapping table based on keyword analyses of the content items.

16. The method of claim 14, wherein generating the trend data for the topic comprises generating, for the topic, multiple count values that each correspond to a different respective user device classification.