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Music recommendation algorithms based on knowledge graph and multi-task feature learning.

Xinqiao Liu1, Zhisheng Yang2, Jinyong Cheng3

  • 1School of Music, Qufu Normal University, Rizhao, 276826, China.

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This study introduces the MMSS_MKR framework to enhance music recommendations by integrating knowledge graphs. The model effectively addresses sparsity and cold start issues, leading to more accurate music suggestions.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Information Retrieval

Background:

  • Music recommendation systems often suffer from data sparsity and cold start problems.
  • Auxiliary information is crucial for improving the accuracy of music recommendations.

Purpose of the Study:

  • To propose an end-to-end framework, MMSS_MKR, that leverages knowledge graphs for music recommendation.
  • To address sparsity and cold start challenges in music recommendation.

Main Methods:

  • The MMSS_MKR framework utilizes a knowledge graph as a source of auxiliary information.
  • Cross & Compression Units bridge knowledge graph embedding and recommendation tasks.
  • The model refines triple information using both knowledge graph and recommendation modules.
  • Multiple predictions and calculations are employed in recommendation and embedding modules, respectively.
  • An improved loss function enhances information utility for recommendations.

Main Results:

  • The MMSS_MKR model demonstrated significant improvements over existing recommendation models.
  • The framework effectively integrates knowledge graph embeddings with recommendation tasks.
  • The approach successfully filters out false triple information, leading to more realistic data.

Conclusions:

  • MMSS_MKR offers a robust solution for music recommendation challenges.
  • Knowledge graph integration enhances the accuracy and effectiveness of music recommendation systems.
  • The proposed framework provides a valuable contribution to the field of personalized recommendations.