Personalized Content Recommendation Engine for TV EPGs
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Solution Overview
Problem
Conventional electronic program guides (EPGs) for television systems are limited in recommending TV programs based solely on channel and chronology, failing to provide personalized content suggestions tailored to viewers' preferences and viewing history.
Innovation Solution
A system architecture comprising a remote control device, media box, Log Collecting Server, Metrics and Profile Server, Content Recommendation Engine, and Ad Server that tracks viewer habits, stores preferences, and generates personalized content recommendations based on viewing history, preferences, and patterns, allowing for efficient navigation and enhanced TV-watching experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional EPGs display shows based on channel and chronology only, then the system structure remains simple, but the personalization capability is insufficient
Solution Approach 1:
The system is divided into multiple independent components: a viewing history collection module that gathers user data, a recommendation engine that processes the data, and an EPG display module that shows results. This segmentation allows the personalization function to be added without completely redesigning the traditional EPG system.
Solution Approach 2:
A recommendation engine acts as an intermediary between the viewing history data and the EPG display. This mediator processes user preferences and generates personalized recommendations, bridging the gap between raw data and personalized content without requiring direct integration between all system components.
2Loss of information
If viewing history tracking is implemented, then personalized recommendations can be provided, but data collection and processing requirements increase
Solution Approach 1:
The system collects and stores viewing history data in advance before recommendation generation is needed. By pre-collecting user viewing patterns, preferences, and habits, the system reduces the processing burden during real-time recommendation generation, as the data preparation work has already been completed.
3Ease of operation
If personalized recommendations are added to EPG, then viewer experience is enhanced, but navigation time may increase due to additional options
Solution Approach 1:
The system displays a limited number of top personalized recommendations rather than showing all possible matching programs. By presenting only the most relevant few options based on user preferences, the system enhances personalization while preventing information overload that would increase navigation time.
Data Source
AI summary
A system and method for providing personal content recommendations comprising a receiver to receive and collect one or more user commands at one or more modules, a processor to generate, at the one or more modules, one or more personalized recommendations based on the one or more user commands, and a transmitter to transmit the one or more personalized recommendations to be displayed at a display device in response to receiving one or more user inputs to display the one or more personalized recommendations.