PERCENT-CONSUMED WEIGHTED CONTENT RECOMMENDATION

• US 20140074858A1
• Filed: 09/07/2012
• Published: 03/13/2014
• Est. Priority Date: 09/07/2012
• Status: Abandoned Application

• Current Assignee: MobiTV Incorporated (Adeia Inc.)
• Original Assignee: MobiTV Incorporated (Adeia Inc.)
• Inventors: Kalmes, Chad, Jacobson, Mark, Lynch, Tim
• Application Number:
  US13/607,621
• Publication Number:
  US 20140074858A1
• Time in Patent Office: Days
• US Class Current: 707/748
• CPC Class Codes: G06F 16/437 Administration of user profiles, e.g. generation, initialisation, adaptation, distribution

Abstract

Techniques and mechanisms described herein facilitate the performance of percent-consumed weighted content recommendation. According to various embodiments, input data for performing media content recommendation analysis may be identified. The input data may describe the presentation of a plurality of media content items in association with a plurality of content management accounts. The input data may comprise a plurality of data points. Each of the data points may identify a respective portion of a respective one of the media content items presented in association with a respective one of the content management accounts. For each or selected ones of the data points, a respective weighting factor may be applied based on the respective portion of the respective media content item presented in association with the respective content management account. The weighted input data may be numerically modeled to produce a plurality of media content recommendations.

Claims

1. A method comprising:

identifying input data for performing media content recommendation analysis, the input data describing the presentation of a plurality of media content items in association with a plurality of content management accounts, the input data comprising a plurality of data points, each of the data points identifying a respective portion of a respective one of the media content items presented in association with a respective one of the content management accounts;

for each or selected ones of the data points, applying a respective weighting factor based on the respective portion of the respective media content item presented in association with the respective content management account; and

storing on a storage system a plurality of media content recommendations produced by numerically modeling the weighted input data, each of the media content recommendations identifying a respective one of the media content items and a respective one of the content management accounts.

2. The method recited in claim 1, wherein the plurality of data points includes a first data point that identifies a first portion of a first content item viewed in association with a first one of the content management account and a second data point that identifies a second portion of the first content item viewed in association with a second one of the content management accounts, and wherein the first portion is larger than the second portion, and wherein the weighting factors associated with the first and second data points render the first data point more significant than the second data point.

3. The method recited in claim 1, wherein applying a respective weighting factor comprises:

applying an initial weighting factor based on the respective portion of the respective media content item presented in association with the respective content management account, and

applying a mathematical transformation to the initial weighting factor.

4. The method recited in claim 1, wherein numerically modeling the weighted input data comprises assigning, for each weighting factor, a respective numerical significance to the respective data point that correlates with the weighting factor.

5. The method recited in claim 1, wherein each or selected ones of the media content items comprises a streaming video capable of being transmitted from a server to a client machine via a network.

6. The method recited in claim 1, wherein the respective portion of the respective media content item identifies a percentage of the media content item that was viewed in association with the respective content management account.

7. The method recited in claim 1, wherein each media content recommendation comprises an estimate of a preference for the respective media content item and the respective content management account.

8. The method recited in claim 1, wherein the media content item is an item selected from the group consisting of:

a video object, a media content genre, a media content category, and a media content channel.

9. A system comprising:

a storage system operable to store input data for performing media content recommendation analysis, the input data describing the presentation of a plurality of media content items in association with a plurality of content management accounts, the input data comprising a plurality of data points, each of the data points identifying a respective portion of a respective one of the media content items presented in association with a respective one of the content management accounts; and

a processor operable to;

apply, for each or selected ones of the data points, a respective weighting factor based on the respective portion of the respective media content item presented in association with the respective content management account; and

numerically model the weighted input data to produce a plurality of media content recommendations, each of the media content recommendations identifying a respective one of the media content items and a respective one of the content management accounts.

10. The system recited in claim 9, wherein the plurality of data points includes a first data point that identifies a first portion of a first content item viewed in association with a first one of the content management account and a second data point that identifies a second portion of the first content item viewed in association with a second one of the content management accounts, and wherein the first portion is larger than the second portion, and wherein the weighting factors associated with the first and second data points render the first data point more significant than the second data point.

11. The system recited in claim 9, wherein applying a respective weighting factor comprises:

applying an initial weighting factor based on the respective portion of the respective media content item presented in association with the respective content management account, and

applying a mathematical transformation to the initial weighting factor.

12. The system recited in claim 9, wherein numerically modeling the weighted input data comprises assigning, for each weighting factor, a respective numerical significance to the respective data point that correlates with the weighting factor.

13. The system recited in claim 9, wherein each or selected ones of the media content items comprises a streaming video capable of being transmitted from a server to a client machine via a network.

14. The system recited in claim 10, wherein the respective portion of the respective media content item identifies a percentage of the media content item that was viewed in association with the respective content management account.

15. The system recited in claim 10, wherein each media content recommendation comprises an estimate of a preference for the respective media content item and the respective content management account

16. The system recited in claim 10, wherein the media content item is an item selected from the group consisting of:

a video object, a media content genre, a media content category, and a media content channel.

17. One or more computer readable media having instructions stored thereon for performing a method, the method comprising:

identifying input data for performing media content recommendation analysis, the input data describing the presentation of a plurality of media content items in association with a plurality of content management accounts, the input data comprising a plurality of data points, each of the data points identifying a respective portion of a respective one of the media content items presented in association with a respective one of the content management accounts;

for each or selected ones of the data points, applying a weighting factor based on the respective portion of the respective media content item presented in association with the respective content management account; and

storing on a storage system a plurality of media content recommendations produced by numerically modeling the weighted input data, each of the media content recommendations identifying a respective one of the media content items and a respective one of the content management accounts.

18. The one or more computer readable media recited in claim 17, wherein the plurality of data points includes a first data point that identifies a first portion of a first content item viewed in association with a first one of the content management account and a second data point that identifies a second portion of the first content item viewed in association with a second one of the content management accounts, and wherein the first portion is larger than the second portion, and wherein the weighting factors associated with the first and second data points render the first data point more significant than the second data point.

19. The one or more computer readable media recited in claim 17, wherein applying a respective weighting factor comprises:

applying an initial weighting factor based on the respective portion of the respective media content item presented in association with the respective content management account, and

applying a mathematical transformation to the initial weighting factor

20. The one or more computer readable media recited in claim 17, wherein numerically modeling the weighted input data comprises assigning, for each weighting factor, a respective numerical significance to the respective data point that correlates with the weighting factor.