Media Recommendation System Using Contextual Interest Prediction
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Solution Overview
Problem
Users face overwhelming choices when selecting media content due to the vast number of options available, with existing recommendation systems often failing to account for nascent media programs or real-time content and requiring excessive time to identify desired programming.
Innovation Solution
A multimedia distribution system that utilizes a client device, database, and server to analyze user viewing history and context, predicting current interest in media programs and providing personalized, temporally and contextually relevant recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users review potential media content to identify desired programming, then they can find content of interest, but they spend an undesirably long amount of time reviewing content
Solution Approach 1:
The system performs preliminary analysis of user viewing history and characteristics before the user needs to make a selection. By pre-processing usage information and determining viewing characteristics in advance, the system prepares personalized recommendation profiles that enable rapid content identification without requiring users to spend time reviewing options manually
Solution Approach 2:
The recommendation system automatically analyzes user behavior patterns and generates personalized content recommendations without requiring active user participation. The system serves itself by using its own collected usage information to generate recommendations, eliminating the need for users to manually search or review content
2Adaptability or versatility
If existing recommendation systems present multiple options across categories, then users have variety to choose from, but users still spend undesirable time identifying a program of interest
Solution Approach 1:
The system applies different recommendation strategies to different contexts and user states. By analyzing specific viewing characteristics and current context, the system tailors recommendations to match user preferences and situation, providing locally optimized suggestions rather than generic category-based options
Solution Approach 2:
The system pre-determines viewing characteristics by analyzing usage information before presenting recommendations. This preliminary analysis allows the system to predict user interest and prioritize recommendations accordingly, reducing the time users need to spend evaluating options
3Ease of operation
If recommendation systems provide general programming suggestions, then users receive guidance, but the recommendations do not account for nascent or real-time media programs that could be of interest
Solution Approach 1:
The recommendation system dynamically adapts to include nascent and real-time content by continuously analyzing current usage information and updating viewing characteristics. The system adjusts its recommendations based on emerging patterns in user behavior and newly available content, ensuring recommendations remain current and relevant
Solution Approach 2:
The system uses feedback from actual user viewing behavior to refine and update recommendations in real-time. By monitoring usage information and predicting current interest based on viewing characteristics, the system continuously improves its ability to recommend nascent and real-time programs that match user preferences
Data Source
AI summary
Multimedia systems and related methods and devices are provided for recommending media programs to a user. Viewing characteristics of the user are determined based on usage information detailing preceding viewing sessions for the user. In one or more embodiments, the user's current interest in one or more currently available media programs that originated after the user's preceding viewing session is predicted based on the user's viewing characteristics and the current viewing context, and media programs having the highest predicted current interest are indicated to the user as being recommended.