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Sequence-Based Explainable Hybrid Song Recommendation.

Khalil Damak1, Olfa Nasraoui1, William Scott Sanders2

  • 1Knowledge Discovery and Web Mining Lab, Department of Computer Science and Engineering, University of Louisville, Louisville, KY, United States.

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|August 16, 2021
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Summary
This summary is machine-generated.

This study introduces a hybrid deep learning model for song recommendations that leverages sequential song content and collaborative filtering (CF). The model provides accurate, explainable recommendations and effectively addresses the item cold start problem.

Keywords:
collaborative filteringdeep learningexplainabilityhybrid recommender systemitem cold start problemrecurrent neural networkssong recommendationtransparency and fairness in AI

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Area of Science:

  • Computer Science
  • Music Information Retrieval
  • Artificial Intelligence

Background:

  • Existing deep learning song recommendation systems often neglect song content's sequential nature.
  • There's a need for explainable recommendations and methods to overcome the item cold start problem in music recommendation.

Purpose of the Study:

  • To propose a hybrid deep learning model for enhanced song recommendation.
  • To provide accurate, personalized, and explainable song recommendations.
  • To address the item cold start challenge in music recommendation.

Main Methods:

  • Developed a hybrid deep learning model integrating collaborative filtering (CF) and sequence models.
  • Utilized Musical Instrument Digital Interface (MIDI) content for song analysis.
  • Incorporated personalized explanation generation based on song content.

Main Results:

  • The proposed model achieved top performance in recommendation accuracy compared to state-of-the-art methods.
  • Successfully generated explainable recommendations and effectively handled the item cold start problem.
  • Personalized explanations were validated to align with user preferences.

Conclusions:

  • The hybrid deep learning model offers a significant advancement in explainable and accurate song recommendation.
  • The model's ability to utilize sequential song content and address cold start issues makes it highly effective.
  • Personalized, content-based explanations enhance user trust and satisfaction in recommended music.