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Adaptive genetic algorithm for user preference discovery in multi-criteria recommender systems.

Mohammed Wasid1, Rashid Ali1,2, Sana Shahab3

  • 1Interdisciplinary Centre for Artificial Intelligence, Aligarh Muslim University, Aligarh, India.

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Summary
This summary is machine-generated.

This study introduces a novel genetic algorithm to uncover user preferences in Multi-Criteria Recommender Systems (MCRS). The adaptive approach effectively identifies user tastes across multiple criteria, improving recommendation accuracy.

Keywords:
Collaborative filteringGenetic algorithmsMulti-criteria decision makingNormalized rating countRecommender systems

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Area of Science:

  • Artificial Intelligence
  • Computer Science
  • Information Retrieval

Background:

  • Multi-Criteria Recommender Systems (MCRS) leverage user preferences across various product factors for effective recommendations.
  • Eliciting precise user preferences remains a significant challenge in MCRS due to the complexity of multi-criteria product evaluation.

Purpose of the Study:

  • To propose a novel three-phase adaptive genetic algorithm for discovering user preferences in Multi-Criteria Recommender Systems.
  • To enhance the accuracy of product recommendations by accurately modeling user preferences on multiple criteria.

Main Methods:

  • A three-phase adaptive genetic algorithm approach is developed to model user preferences.
  • Weights are assigned to multi-criteria features, and user preferences are learned via a genetic algorithm during similarity computation.
  • Product recommendations are generated based on predicted user preferences.

Main Results:

  • The proposed genetic algorithm-based approach significantly outperforms traditional multi-criteria and single-criteria recommender systems.
  • Demonstrated superior performance on the Yahoo! Movies dataset across various evaluation metrics.
  • Successfully identified like-minded users by accurately capturing individual preferences.

Conclusions:

  • The adaptive genetic algorithm effectively addresses the challenge of preference elicitation in MCRS.
  • This method enhances recommendation quality by accurately reflecting user preferences across multiple criteria.
  • The approach offers a promising direction for developing more sophisticated and personalized recommender systems.