Music Recommendation Interface Using Associated User Discovery
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Solution Overview
Problem
Current music pushing methods rely heavily on user proactive behavior and relationships, failing to effectively expand music discovery for new users or those less proactive, leading to repetitive and centralized recommendations.
Innovation Solution
Display user information that satisfies a preset association relationship with currently playing music upon user interaction, including both friend and stranger users who have similar music preferences, to broaden music discovery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If music pushing relies on user proactive behavior and friend relationships, then pushing accuracy for active users is improved, but music discovery diversity deteriorates
Solution Approach 1:
The patent introduces a dual-channel pushing mechanism where AI algorithm pushing serves as an intermediary to complement friend relationship pushing. The system determines whether to use AI pushing or friend pushing based on user activity level, ensuring that inactive users receive diverse music recommendations through AI while active users continue to benefit from friend-based recommendations, thus resolving the contradiction between pushing accuracy and music discovery diversity
Solution Approach 2:
The system dynamically changes the pushing parameter (pushing channel selection) based on user activity status. For inactive users, the system switches to AI algorithm pushing with adjusted parameters (e.g., broader music scope, different weighting), while for active users, it maintains friend relationship pushing. This parameter adaptation resolves the contradiction by tailoring the pushing strategy to user behavior patterns
2Measurement precision
If music pushing depends on friend relationships, then personalization is improved, but pushing coverage deteriorates
Solution Approach 1:
The patent implements a universal pushing system that can function in multiple modes: friend relationship pushing for active users and AI algorithm pushing for inactive users. The AI pushing module serves as a universal backup that can recommend music to any user regardless of their friend network size or activity level, thus expanding pushing coverage while maintaining personalization through user-specific algorithm adjustments
3Measurement precision
If music pushing uses historical data analysis, then recommendation accuracy is improved, but user experience deteriorates due to excessive error correction intervention
Solution Approach 1:
The system performs preliminary action by proactively identifying inactive users and switching them to AI algorithm pushing before they experience the problems of friend-based pushing (repetitiveness, limited coverage). This preliminary intervention prevents the need for users to manually correct errors or repeatedly search for new music, thereby maintaining high recommendation accuracy while improving user experience by eliminating the need for error correction interventions
Data Source
AI summary
Music pushing method, apparatus, electronic device, and storage medium are provided by the embodiments of the present disclosure, for the method, firstly, a user end displays user information that satisfies a preset association relationship with music currently on a music playing interface in response to an operation acting on the music playing interface. In the present embodiments, upon inputting an operation instruction on target music by a user, information of other users who have taken an associated operation instruction to the target music and have an unrestricted friend relationship with the user is displayed, and by presenting music lists corresponding to other users, the user is guided to acquire music pushing information, and thus, ways for the user to acquire the music pushing information are expanded, and problems that music pushing is single and centralized as well as the pushing depends on a friend relationship are overcome.