Sequence-Aware User Type Estimation Model for Karaoke Services
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Solution Overview
Problem
Existing user clustering methods for karaoke services do not adequately consider the sequence of musical pieces sung, leading to inadequate user type classification.
Innovation Solution
A type estimation model generation system that uses machine learning to generate a type estimation model based on time series information of musical pieces sung, allowing for sequence-aware user type classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user clustering is performed based on singing histories without considering sequence, then clustering can be performed with simpler processing, but user type classification becomes inaccurate
Solution Approach 1:
The patent segments the singing history into a sequence of individual musical piece selections, processing each piece as a discrete unit in time order. This segmentation allows the system to capture the sequential pattern of user preferences while maintaining manageable processing complexity through structured iteration over time steps.
Solution Approach 2:
The patent performs preliminary processing by pre-defining the time series structure and preparing the sequence data before clustering. The system establishes the temporal framework and organizes musical piece information in advance, which simplifies the subsequent clustering process while ensuring sequence-aware analysis.
2Measurement precision
If sequence information of musical pieces is considered in clustering, then user type classification becomes more accurate, but processing time and complexity increase
Solution Approach 1:
The patent implements periodic processing by iterating through the time series data in discrete time steps, processing one musical piece at a time in sequence. This periodic approach systematically handles the sequence information without requiring complex parallel processing, balancing accuracy with processing time through structured temporal iteration.
Solution Approach 2:
The patent maintains continuity of useful action by processing the time series sequence continuously from the first musical piece to the last, ensuring that each piece contributes to the cumulative user profile. This continuous sequential processing captures the evolution of user preferences over time while avoiding interruptions or complex batch operations.
Data Source
AI summary
A type estimation model generation system is a system that generates a type estimation model used for type estimation of estimating one of a plurality of types to which a user belongs, the system including: a learning data acquiring unit configured to acquire learning time series information that is information of a time series about a plurality of used musical pieces and learning type information representing types to which users who have used the plurality of musical pieces belong that are learning data used for machine learning; and a model generating unit configured to generate the type estimation model by performing machine learning using information based on the learning time series information as an input for the type estimation model in units of musical pieces in order of the time series and information based on the learning type information as an output of the type estimation model.