Personalized Music Recommendation via Visual Semantic Tags
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Solution Overview
Problem
Existing music recommendation systems provide non-personalized services to users as they rely on fixed matching relationships between materials and music, failing to offer differentiated recommendations based on user preferences.
Innovation Solution
A method that determines visual semantic tags of a material, identifies matched music from a candidate library, sorts and recommends music based on user assessing information, and applies preset screening conditions to provide personalized music recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed matching relationship between materials and music is used for recommendations, then the recommendation process is simple and fast, but the service cannot be personalized for different users
Solution Approach 1:
The patent transforms the static fixed matching relationship into a dynamic recommendation system that adapts to different users. It introduces user assessing information and sorting mechanisms that change recommendations based on user preferences, making the system flexible and adaptive rather than rigid and uniform
Solution Approach 2:
The patent changes the recommendation parameters by introducing user-specific assessing information (such as user preferences, historical behavior, or assessment data) into the matching process. This allows the same material to be matched with different music for different users, achieving personalization while maintaining the efficiency of automated matching
2Adaptability or versatility
If user assessing information and sorting mechanisms are introduced for personalization, then personalized recommendations are achieved, but processing resources and bandwidth consumption increase
Solution Approach 1:
The patent performs preliminary actions by pre-collecting and organizing user assessing information and pre-establishing sorting criteria. This preparation work is done in advance, allowing the actual recommendation process to efficiently utilize pre-computed data rather than performing complex calculations in real-time, thus reducing processing resource consumption during user interactions
Solution Approach 2:
The patent uses copying by creating simplified representations or profiles of user preferences based on assessing information. Instead of processing all raw user data repeatedly, the system creates compact user profiles that capture essential preferences, enabling efficient matching while maintaining personalization
Data Source
AI summary
This application discloses a method for making music recommendations. The method for making music recommendations is performed by a server device. The method includes obtaining a material for which background music is to be added; determining at least one visual semantic tag of the material, the at least one visual semantic tag describing at least one characteristic of the material; identifying a matched music matching the at least one visual semantic tag from a candidate music library; sorting the matched music according to user assessing information of a user corresponding to the material; screening the matched music based on a sorting result and according to a preset music screening condition; and recommending matched music obtained through the screening as candidate music of the material.