An enhanced integrated fuzzy logic-based deep learning techniques (EIFL-DL) for the recommendation system on...

Area of Science:

• Artificial Intelligence
• Machine Learning
• Data Science

Background:

• Recommender systems (RSs) are crucial for personalization in industrial settings.
• Traditional RSs struggle with the complexity and uncertainty of rapidly evolving industrial data.
• There is a need for advanced techniques to improve recommendation accuracy and interpretability.

Purpose of the Study:

• To propose an enhanced integrated fuzzy logic-based deep learning (EIFL-DL) framework.
• To address the limitations of traditional RSs in handling industrial data challenges.
• To improve the accuracy and interpretability of recommendations in industrial applications.

Main Methods:

• The EIFL-DL framework integrates fuzzy logic for uncertainty handling and deep learning for pattern extraction.
• Data preprocessing involves cleaning, normalization, and transformation into fuzzy sets.
• Feature extraction utilizes deep learning models like CNNs and RNNs.
• Recommendation generation employs fuzzy logic rules and a hybrid algorithm.

Main Results:

• The EIFL-DL framework effectively handles uncertainty and vagueness in industrial data.
• Deep learning components extract complex patterns for enhanced feature representation.
• The integrated approach overcomes limitations of traditional RSs, yielding accurate and interpretable recommendations.

Conclusions:

• The EIFL-DL framework offers a robust solution for personalized recommendations in industrial environments.
• Combining fuzzy logic and deep learning significantly enhances RS performance.
• This approach provides a foundation for more sophisticated industrial recommender systems.