Podcasts in Personalized Content Streams

• US 20150381682A1
• Filed: 06/30/2014
• Published: 12/31/2015
• Est. Priority Date: 06/30/2014
• Status: Active Grant

• Current Assignee: R2 Solutions LLC (Acacia Research Corporation)
• Original Assignee: Yahoo! Inc. (Apollo Global Management, Inc.)
• Inventors: Rao, Supreeth, Narravula, Sundeep, Ningappa, Shivakumar
• Granted Patent: US 9,661,100 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/972 Access to data in other repository systems, e.g. legacy data or dynamic Web page generation
• CPC Class Codes: H04L 67/60 Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources admission control or resource allocation H04L47/70
• CPC Class Codes: H04N 21/251 Learning process for intelligent management, e.g. learning user preferences for recommending movies details of learning user preferences for the retrieval of video data in a video database G06F16/739; computer systems using learning methods G06N3/08
• CPC Class Codes: H04N 21/25891 being end-user preferences retrieval of video data in a video database based on user preferences G06F16/739; arrangements for recognizing users' preferences H04H60/46; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18
• CPC Class Codes: H04N 21/2668 Creating a channel for a dedicated end-user group, e.g. insertion of targeted commercials based on end-user profiles information retrieval from the Internet by querying with filtering and personalisation G06F16/9535; arrangements for replacing or switching information during the broadcast H04H20/10; push services over packet-switching network H04L12/1859; adaptation of message content in packet-switching networks H04L51/063
• CPC Class Codes: H04N 21/812 involving advertisement data advertising per se G06Q30/02
• CPC Class Codes: H04N 21/8352 involving content or source identification data, e.g. Unique Material Identifier [UMID]
• CPC Class Codes: H04N 21/8586 by using a URL processing chained hypermedia data for information retrieval G06F16/94; information retrieval from the Internet by using URLs G06F16/955; URL in broadcast information H04H20/93; Web-based protocols H04L67/02

Abstract

Software on a content-aggregation website obtains a resource associated with a podcast from a website publishing the podcast and stores it e resource on the content-aggregation website. The software adds the resource as a leaf node to a taxonomy generated by the content-aggregation website. The addition is based on data associated with the podcast. The non-leaf nodes in the taxonomy are categories of content. The software determines that a user of the content aggregation website is qualified as to at least one category that includes the resource as a leaf node. The determination is based at least in part on feedback from the user that includes a viewing or listening history for the user. Then the software serves the resource to the user in a content stream published by the content-aggregation website, based at least in part on a personalization score associated with the resource.

Claims

8-1. The computer-readable media of claim 11, wherein the taxonomy is generated by the second website using supervised classifiers.

11. One or more computer-readable media persistently storing instructions that, when executed by a processor, perform the following operations:

obtain a resource associated with a podcast from a first website publishing the podcast;

store the resource on a second website that ingests content;

add the resource as a leaf node to a taxonomy generated by the second website, wherein the addition is based at least in part on data associated with the podcast and wherein the non-leaf nodes in the taxonomy are categories of content;

determine that a user of the second website is qualified as to at least one category that includes the resource as a leaf node, wherein the determination is based at least in part on feedback from the user that includes one or more of a viewing history for the user or a listening history for the user; and

serve the resource to the user in a content stream published by the second website, based at least in part on a personalization score associated with the resource.

1. A method, comprising operations of:

obtaining a resource associated with a podcast from a first website publishing the podcast;

storing the resource on a second website that ingests content;

adding the resource as a leaf node to a taxonomy generated by the second website, wherein the addition is based at least in part on data associated with the podcast and wherein the non-leaf nodes in the taxonomy are categories of content;

determining that a user of the second website is qualified as to at least one category that includes the resource as a leaf node, wherein the determination is based at least in part on feedback from the user that includes one or more of a viewing history for the user or a listening history for the user; and

serving the resource to the user in a content stream published by the second website, based at least in part on a personalization score associated with the resource, wherein each of the operations are executed by one or more processors.

2. The method of claim 1, wherein the resource is an audio or video file and the podcast is a paid podcast.

3. The method of claim 1, wherein the resource is a uniform resource locator (URL) for a web feed and the podcast is a free podcast.

4. The method of claim 1, wherein the determination that the user of the second website is qualified as to at least one category is based at least in part on calculation and application of a similarity measure.

5. The method of claim 1, wherein the data associated with the podcast includes data associated with the source of the podcast.

6. The method of claim 1, wherein the data associated with the podcast includes data or metadata from the web pages of the first website.

7. The method of claim 1, wherein addition of the resource as a leaf to the taxonomy includes analyzing an audio or video file associated with the resource.

8. The method of claim 1, wherein the taxonomy is generated by the second website using supervised classifiers.

9. The method of claim 1, wherein the personalization score is based at least in part on a parametric function that includes a measure of user fatigue as a parameter.

10. The method of claim 1, wherein the personalization score results at least in part from an analysis of revenue or engagement.

12. The computer-readable media of claim 11, wherein the resource is an audio or video file and the podcast is a paid podcast.

13. The computer-readable media of claim 11, wherein the resource is a uniform resource locator (CTRL) for a web feed and the podcast is a free podcast.

14. The computer-readable media of claim 11, wherein the determination that the user of the second website is qualified as to at least one category is based at least in part on application of a similarity measure.

15. The computer-readable media of claim 11, wherein the data associated with the podcast includes data associated with the source of the podcast.

16. The computer-readable media of claim 11, wherein the data associated with the podcast includes data or metadata from the web pages of the first website.

17. The computer-readable media of claim 11, wherein addition of the resource as a leaf to the taxonomy includes analyzing an audio or video file associated with the resource.

19. The computer-readable media of claim 11, wherein the personalization score is based at least in part on a parametric function that includes a measure of user fatigue as a parameter.

20. A method, comprising operations of:

obtaining a resource associated with a podcast from a first website publishing the podcast;

storing the resource on a second website that ingests content;

adding the resource as a leaf node to a taxonomy generated by the second website, wherein the addition is based at least in part on data associated with the podcast, wherein the non-leaf nodes in the taxonomy are categories of content based at least in part on data associated with the podcast, wherein the taxonomy can be represented as a B+ tree, and wherein the taxonomy is generated by the second website using supervised classifiers;

determining that a user of the second website is qualified as to at least one category that includes the resource as a leaf node, wherein the determination is based at least in part on feedback from the user that includes one or more of a viewing history for the user or a listening history for the user; and

serving the resource to the user in a content stream published by the second website, based at least in part on a personalization score associated with the resource, wherein each of the operations are executed by one or more processors.