Multimedia Content Recommendation via User Segmentation
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Solution Overview
Problem
Existing content and sound source recommendation technologies fail to effectively recommend personalized content to multiple users sharing a single device, such as a TV, without requiring individual user profiles, and often recommend unsuitable sound sources due to inefficiencies in analyzing entire musical compositions and relying solely on user preferences.
Innovation Solution
A device and method that classify channels into groups based on attributes and user viewing histories, generating content recommendations for each group, and determine user intent through sound source usage history, lyrics, and scores to provide personalized sound source recommendations with reasons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If content recommendation is based on integrated viewing history for a single device, then the system can operate without individual user profiles, but it recommends content that does not match the preferences of specific household members
Solution Approach 1:
The patent segments the household users into different user groups based on their viewing patterns and preferences. Instead of treating all users as a single entity, the system divides them into segments (e.g., morning viewers, evening viewers, different age groups) and creates separate preference profiles for each segment. This allows the recommendation system to provide personalized content suggestions while maintaining ease of operation without requiring explicit user registration.
Solution Approach 2:
The system implements self-service by automatically analyzing viewing history data and generating user group profiles without requiring manual user registration or profile creation. The viewing patterns are automatically clustered and segmented, and the system serves recommendations based on these automatically generated profiles, eliminating the need for user intervention while maintaining personalization accuracy.
2Measurement precision
If music recommendation analyzes entire musical compositions, then comprehensive analysis is achieved, but processing efficiency becomes too low
Solution Approach 1:
The patent extracts and analyzes only the most relevant features from musical compositions rather than processing the entire composition. It identifies and extracts key characteristics such as tempo, genre, mood, and structural elements that are most important for recommendation purposes. This selective extraction maintains comprehensive analysis of essential musical attributes while dramatically reducing processing time and computational resources required.
3Adaptability or versatility
If sound source recommendation relies only on user preferences, then personalization is achieved, but unsuitable sound sources are frequently recommended
Solution Approach 1:
The patent implements a feedback mechanism that continuously monitors user interactions with recommended sound sources. When users reject or skip recommended music, the system learns from this feedback and adjusts future recommendations. It analyzes patterns in user behavior (such as skipping certain genres or artists) and refines the recommendation algorithm to avoid similarly unsuitable suggestions, thereby improving recommendation suitability while maintaining personalization.
Data Source
AI summary
The present invention relates to a device and a method for recommending content and a sound source. The present invention, especially in a multimedia device such as a TV which can be used by a plurality of users, can generate channel groups in accordance with channel properties, can recommend appropriate content for each channel group by analyzing users' viewing history type for each channel group, and can acquire user's intent on the basis of user's use history of a sound source, lyrics and music information of the sound source, and the like, thereby providing, on the basis of the user's intent, sound source recommendation information and various reasons for sound source recommendation with respect to the sound source recommendation information.