ENHANCED CONTENT QUALITY USING CONTENT FEATURES

• US 20170076318A1
• Filed: 09/16/2015
• Published: 03/16/2017
• Est. Priority Date: 09/16/2015
• Status: Active Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Inventors: Goswami, Abhishek, Paravastu, Rohit, Ding, Wen, Kwak, Woohee
• Application Number:
  US14/856,236
• Publication Number:
  US 20170076318A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 30/0245 Surveys
• CPC Class Codes: G06Q 30/0251 Targeted advertisements
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 30/0276 Advertisement creation

Abstract

Architecture that includes an automated content recommendation engine to enhance the quality of content being delivered, and machine learning for the content recommendation. Content recommendation is improved for users and the dissatisfaction rate of displayed content lowered, thereby improving the overall user experience. More specifically, the architecture enables and obtains direct user feedback (e.g., like/dislike) and crowdsourced user feedback of the content, merges content features with the user feedback to build the content recommendation engine, uses the content recommendation engine to detect other content that the user may like or dislike, and, thereby reduces the DSAT rate of display content using the content recommendation engine.

Claims

1. A content recommendation system, comprising:

a content features component configured to enable assignment of features to a piece of content;

a feedback component configured to automatically submit the piece of content and the features to a user feedback system and receive user feedback about the piece of content;

a scoring component configured to generate a score for the piece of content based on the user feedback;

a quality component configured to compute content quality of the piece of content based on the score; and

at least one hardware processor configured to execute computer-executable instructions in a memory, the instructions executed to enable the content features component, the feedback component, the scoring component, and the quality component.

2. The system of claim 1, wherein the scoring component is configured to score the piece of content based on various features.

3. The system of claim 1, wherein the feedback component submits the piece of content in an application user interface in which the piece of content can be used.

4. The system of claim 1, wherein the feedback component submits the piece of content to the user feedback system and one or more other user feedback systems.

5. The system of claim 1, wherein the content features component is configured to consider application-centric features of an application user interface in which the piece of content can be displayed.

6. The system of claim 1, wherein the content quality is interpreted to eliminate lower quality content and retain higher quality content.

7. The system of claim 1, wherein the content features comprise at least one of global features that represent overall content quality, local features that represent specific parts within a given user interface, or contextual features that represent visual features in context of a publisher application.

8. The system of claim 1, wherein the user feedback system enables the user feedback via at least one of direct user feedback and crowdsourced user feedback.

9. The system of claim 1, further comprising a score consolidation component configured to consolidate feature scores of the features and to generate the score.

10. A content recommendation method, comprising acts of:

automatically submitting an item of content and features of the item of content to a crowdsourcing system;

receiving direct user feedback about the item of content from the crowdsourcing system;

generating a score for the item of content based on the direct user feedback;

computing content quality of the item of content based on the score; and

recommending a new item of content based on the content quality.

11. The method of claim 10, further comprising generating a task visual that identifies and presents to a reviewer of the crowdsourcing system features about the item of content of which reviewer response is requested.

12. The method of claim 10, further comprising generating a task visual as part of a publisher application user interface that embeds the item of content in the user interface and, identifies and presents to a reviewer of the crowdsourcing system features about the item of content of which reviewer response is requested.

13. The method of claim 10, further comprising signaling a content delivery engine to cease serving the item of content based on the content quality.

14. The method of claim 10, further comprising consolidating feature scores of the visual features to generate the score.

15. The method of claim 10, further comprising eliminating or retaining the item of content based on a feature score relative to a threshold.

16. A content recommendation method, comprising acts of:

automatically submitting an advertisement and visual features of the advertisement as a task to one or more a crowdsourcing systems;

receiving direct user feedback about the visual features of the advertisement from reviewers of the one or more crowdsourcing systems;

generating scores for each of the visual features;

consolidating the scores into an overall score for the advertisement;

computing quality of the advertisement based on the overall score; and

eliminating or retaining the advertisement based on the quality.

17. The method of claim 16, further comprising recommending a new advertisement based on the quality.

18. The method of claim 16, further comprising predicting ratings of each of the reviewers using machine learning.

19. The method of claim 16, further comprising adapting and embedding the advertisement in different publisher application contexts and predicting scores for new advertisements for a given reviewer.

20. The method of claim 16, further comprising selecting the visual features from at least one of a global level, local level, or a contextual level.