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Neural Collaborative Filtering with Ontologies for Integrated Recommendation Systems.

Rana Alaa El-Deen Ahmed¹, Manuel Fernández-Veiga², Mariam Gawich³

¹Business Information System, Arab Academy for Science Technology and Maritime Transport, Cairo B-2401, Egypt.

Sensors (Basel, Switzerland)

|January 22, 2022

Summary

This study introduces a hybrid recommendation system combining ontology-based and machine learning (ML) methods. The novel approach enhances predictions by integrating semantic knowledge with statistical models for improved performance.

Keywords:

deep learning neural collaborative filtering ontologies recommendation systems retail dataset

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Area of Science:

Artificial Intelligence
Computer Science
Information Retrieval

Background:

Machine learning (ML), particularly deep learning (DL), excels in AI tasks but differs from traditional engineering and ontology approaches.
Ontologies rely on domain knowledge and inference rules, while ML uses large datasets and generic algorithms.
Hybrid systems merging ontology-based and ML techniques offer a promising way to enhance semantic knowledge with statistical models.

Purpose of the Study:

To present a novel hybrid recommendation system.
To combine knowledge-driven recommendations from a tailored ontology with data-driven recommendations from classifiers and neural collaborative filtering.
To demonstrate measurable improvements by integrating semantic and statistical knowledge.

Main Methods:

Developed a hybrid recommendation system architecture.
Integrated classical knowledge-driven recommendations from a tailored ontology.
Incorporated data-driven recommendations using classifiers and neural collaborative filtering.
Enabled transfer of semantic information to ML and statistical knowledge to the ontology.

Main Results:

Achieved measurable improvements by combining knowledge-driven and data-driven recommendation approaches.
Facilitated the transfer of semantic information to ML models.
Enabled the transfer of statistical knowledge back to the ontology.
Captured dynamic user behaviors and product updates within the system.

Conclusions:

The proposed hybrid system effectively blends ontology-based and ML-based recommendation techniques.
This integration enhances prediction accuracy and enables knowledge transfer between semantic and statistical models.
The system successfully captures the dynamic nature of recommendation environments.