Emotion-Based Media Collection Generation via Interactive Playback Interface
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Solution Overview
Problem
Existing media collection generation methods in multimedia applications rely on complex classification logic based on media information, failing to provide intuitive, emotional, and feeling-based classification, which is inefficient and inconvenient for users.
Innovation Solution
A method and apparatus that display emotion identifications within a playback interface, allowing users to interactively add media to emotion-based playlists, simplifying the classification process and eliminating the need for pre-defined playlist names, thereby classifying media based on user emotional feelings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If media classification is performed based on media information using user-defined playlists, then media can be organized into custom playlists, but the classification logic becomes complex and efficiency of media classification is low
Solution Approach 1:
The system automatically generates media collections by analyzing user interaction data and media attributes without requiring manual user definition of playlists. The server autonomously performs classification based on learned patterns from user behavior, eliminating the need for users to create complex classification rules manually.
Solution Approach 2:
The patent replaces manual mechanical classification operations with automated intelligent algorithms. Machine learning models analyze media attributes and user interactions to automatically generate and update media collections, substituting the mechanical process of manual playlist creation with intelligent automated classification.
2Adaptability or versatility
If user-defined playlists are used for media classification, then custom playlists can be created, but the process fails to satisfy user requirements for intuitive-emotional-feeling-based classification
Solution Approach 1:
The system changes the classification parameters from traditional media attributes (genre, artist, album) to emotional and contextual parameters derived from user interaction data. By analyzing user behavior patterns, playback history, and interaction intensity, the system generates collections based on emotional states and usage contexts rather than fixed media metadata.
Solution Approach 2:
The patent introduces an intermediary layer of user interaction data analysis between the user and media classification. Instead of directly classifying media based on its inherent properties, the system uses user interaction patterns as an intermediary to infer emotional states and preferences, then generates collections that reflect these inferred states.
3Manufacturing precision
If complex classification logic is used for media organization, then detailed categorization can be achieved, but the generation steps and logics become complicated
Solution Approach 1:
The patent extracts the complex classification logic from the user interface and relocates it to the server-side recommendation system. The terminal device simply collects user interaction data and receives generated collection results, while the complex analysis and classification algorithms run remotely on the server, separating the simple client operations from complex server processing.
Solution Approach 2:
The system performs preliminary analysis of user interaction data and media attributes in advance to pre-generate media collections before users need them. By continuously analyzing interaction patterns and pre-computing collections based on inferred emotional states and preferences, the system prepares classification results ahead of time, reducing the complexity of real-time classification operations.
Data Source
AI summary
Embodiments of the present disclosure provide a media collection generation method and apparatus, an electronic device, a storage medium, a computer program product, and a computer program, In the media collection generation method, a plurality of emotion identifiers are displayed in a playing interface for a target piece of media, the emotion identifiers being used for representing preset emotion types; and in response to a first interaction operation on a target emotion identifier, adding the target piece of media to a target emotion media collection corresponding to the target emotion identifier. The emotion identifiers that are preconfigured in the playing interface and triggered by means of an interaction operation implement classification of a target piece of media, and consequently generation of corresponding emotion media collections is achieved, causing a generated emotion media collection to achieve media classification on the basis of on the emotion and feeling of a user, the user experience of a personalized media collection for a user is improved, media collection generation steps and logic are simplified, and media collection generation efficiency is improved.