Personalized Recommendation Service for Network-Enabled Television
Solution Overview
Problem
Traditional advertising on commercial television lacks personalized targeting strategies, failing to provide individualized recommendations to viewers based on their consumption habits and preferences.
Innovation Solution
A recommendation service that analyzes user profiles and digital content characteristics, such as metadata from television programs, to generate personalized item recommendations while users are watching content on network-enabled devices, using acquisition, analysis, and presentation modules to provide notifications and links to purchase these items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional commercial television advertising is used, then broad audience reach is achieved, but personalized targeting capability deteriorates
Solution Approach 1:
The patent segments the broad television audience into individual user profiles based on viewing history, purchase behavior, and personal characteristics. Each user receives customized advertising content tailored to their specific interests and preferences, transforming mass media broadcasting into personalized targeted advertising while maintaining broad reach through automated profile-based segmentation.
2Adaptability or versatility
If personalized recommendation services are implemented, then user engagement is improved, but system complexity increases
Solution Approach 1:
The system automatically collects user data from multiple sources (viewing history, purchase records, demographic information) and generates personalized profiles without manual intervention. The complexity of data collection, analysis, and recommendation generation is handled autonomously by the system, reducing the need for complex manual configuration while maintaining high user engagement through personalized content.
3Measurement precision
If real-time analysis of user profiles and content metadata is performed, then recommendation accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary analysis and processing of user profile data and content metadata in advance, building and updating user profiles continuously as new data becomes available. This pre-processing ensures that when a recommendation is needed, the system can quickly retrieve and match pre-analyzed data, maintaining high recommendation accuracy while minimizing real-time processing delays.
Data Source
AI summary
Features are disclosed for providing notifications or recommendations to users based on user profile information and digital content the user is watching. In some implementations, a user can be watching a television program on a network enabled television. The systems and methods may determine characteristics of the television program and obtain user profile information. The systems and methods then may analyze the determined characteristics and user profile information to determine an association between the user and an item of interest in the television program. The systems and methods then may generate and provide a notification relating to the association to the user. Optionally, the systems and methods may also provide one or more recommendations to the user based at least in part on the determined association.