View source for 17948655. ATTRIBUTE-BASED CONTENT RECOMMENDATIONS INCLUDING MOVIE RECOMMENDATIONS BASED ON METADATA simplified abstract (Rovi Guides, Inc.)

=ATTRIBUTE-BASED CONTENT RECOMMENDATIONS INCLUDING MOVIE RECOMMENDATIONS BASED ON METADATA=


==Organization Name==

[[:Category:Rovi Guides, Inc.|Rovi Guides, Inc.]]


[[Category:Rovi Guides, Inc.]]

==Inventor(s)==

[[:Category:Vikram Makam Gupta of Bangalore (IN)|Vikram Makam Gupta of Bangalore (IN)]][[Category:Vikram Makam Gupta of Bangalore (IN)]]

[[:Category:Vishwas Sharadanagar Panchaksharaiah of Tumkur District (IN)|Vishwas Sharadanagar Panchaksharaiah of Tumkur District (IN)]][[Category:Vishwas Sharadanagar Panchaksharaiah of Tumkur District (IN)]]

[[:Category:Reda Harb of Issaquah WA (US)|Reda Harb of Issaquah WA (US)]][[Category:Reda Harb of Issaquah WA (US)]]

==ATTRIBUTE-BASED CONTENT RECOMMENDATIONS INCLUDING MOVIE RECOMMENDATIONS BASED ON METADATA - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17948655 titled 'ATTRIBUTE-BASED CONTENT RECOMMENDATIONS INCLUDING MOVIE RECOMMENDATIONS BASED ON METADATA

==Simplified Explanation==

Improved content recommendations are generated based on a knowledge graph of a content item, which is based on an attribute of the content item, metadata regarding the content item, a viewing history, and user preferences determined by analysis and selected by a user. An option for selecting attributes of interest from a plurality of attributes is generated for display. A content recommendation based on the selected attributes is generated and displayed in a user interface, which changes as user preference selections change. As a result, a user quickly identifies and consumes a customized list of content items related to the user's favorite actor, character, title, depicted object, depicted setting, actual setting, type of action, type of interaction, genre, release date, release decade, director, MPAA rating, critical rating, plot origin point, plot end point, and the like. Related apparatuses, devices, techniques, and articles are also described.

* Content recommendations are personalized based on a knowledge graph of a content item.
* User preferences are determined through analysis and selected by the user.
* The user interface displays content recommendations that change based on user preference selections.

== Potential Applications ==
This technology could be applied in:
* Content streaming platforms
* E-commerce websites
* Social media platforms

== Problems Solved ==
This technology helps solve:
* Information overload for users
* Lack of personalized content recommendations

== Benefits ==
The benefits of this technology include:
* Improved user experience
* Increased user engagement
* Higher user satisfaction

== Potential Commercial Applications ==
The potential commercial applications of this technology include:
* Subscription-based services
* Online retail platforms
* Digital marketing companies

== Possible Prior Art ==
One possible prior art for this technology is the use of collaborative filtering algorithms in recommendation systems. Another could be the use of content-based filtering in personalized recommendations.

### Unanswered Questions
#### How does this technology handle user privacy and data security?
This article does not address the specific measures taken to ensure user data privacy and security in the content recommendation process.

#### What is the scalability of this technology in handling a large number of users and content items?
The scalability of this technology in managing a vast amount of users and content items is not discussed in this article.


==Original Abstract Submitted==

Improved content recommendations are generated based on a knowledge graph of a content item, which is based on an attribute of the content item, metadata regarding the content item, a viewing history, and user preferences determined by analysis and selected by a user. An option for selecting attributes of interest from a plurality of attributes is generated for display. A content recommendation based on the selected attributes is generated and displayed in a user interface, which changes as user preference selections change. As a result, a user quickly identifies and consumes a customized list of content items related to the user's favorite actor, character, title, depicted object, depicted setting, actual setting, type of action, type of interaction, genre, release date, release decade, director, MPAA rating, critical rating, plot origin point, plot end point, and the like. Related apparatuses, devices, techniques, and articles are also described.



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