Neural Collaborative Filtering with Ontologies for Integrated Recommendation Systems
Machine learning (ML) and especially deep learning (DL) with neural networks have demonstrated an amazing success in all sorts of AI problems, from computer vision to game playing, from natural language processing to speech and image recognition. In many ways, the approach of ML toward solving a cla...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/2/700 |
| _version_ | 1827662695933935616 |
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| author | Rana Alaa El-deen Ahmed Manuel Fernández-Veiga Mariam Gawich |
| author_facet | Rana Alaa El-deen Ahmed Manuel Fernández-Veiga Mariam Gawich |
| author_sort | Rana Alaa El-deen Ahmed |
| collection | DOAJ |
| description | Machine learning (ML) and especially deep learning (DL) with neural networks have demonstrated an amazing success in all sorts of AI problems, from computer vision to game playing, from natural language processing to speech and image recognition. In many ways, the approach of ML toward solving a class of problems is fundamentally different than the one followed in classical engineering, or with ontologies. While the latter rely on detailed domain knowledge and almost exhaustive search by means of static inference rules, ML adopts the view of collecting large datasets and processes this massive information through a generic learning algorithm that builds up tentative solutions. Combining the capabilities of ontology-based recommendation and ML-based techniques in a hybrid system is thus a natural and promising method to enhance semantic knowledge with statistical models. This merge could alleviate the burden of creating large, narrowly focused ontologies for complicated domains, by using probabilistic or generative models to enhance the predictions without attempting to provide a semantic support for them. In this paper, we present a novel hybrid recommendation system that blends a single architecture of classical knowledge-driven recommendations arising from a tailored ontology with recommendations generated by a data-driven approach, specifically with classifiers and a neural collaborative filtering. We show that bringing together these knowledge-driven and data-driven worlds provides some measurable improvement, enabling the transfer of semantic information to ML and, in the opposite direction, statistical knowledge to the ontology. Moreover, the novel proposed system enables the extraction of the reasoning recommendation results after updating the standard ontology with the new products and user behaviors, thus capturing the dynamic behavior of the environment of our interest. |
| first_indexed | 2024-03-10T00:32:32Z |
| format | Article |
| id | doaj.art-73aeda2205a347058b6c3b906c92e745 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T00:32:32Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-73aeda2205a347058b6c3b906c92e7452023-11-23T15:23:02ZengMDPI AGSensors1424-82202022-01-0122270010.3390/s22020700Neural Collaborative Filtering with Ontologies for Integrated Recommendation SystemsRana Alaa El-deen Ahmed0Manuel Fernández-Veiga1Mariam Gawich2Business Information System, Arab Academy for Science Technology and Maritime Transport, Cairo B-2401, EgyptatlanTTic, Universidade de Vigo, 36310 Vigo, SpainCentre de Recherche Informatique, Université Française en Egypte (UFE), Cairo 1029, EgyptMachine learning (ML) and especially deep learning (DL) with neural networks have demonstrated an amazing success in all sorts of AI problems, from computer vision to game playing, from natural language processing to speech and image recognition. In many ways, the approach of ML toward solving a class of problems is fundamentally different than the one followed in classical engineering, or with ontologies. While the latter rely on detailed domain knowledge and almost exhaustive search by means of static inference rules, ML adopts the view of collecting large datasets and processes this massive information through a generic learning algorithm that builds up tentative solutions. Combining the capabilities of ontology-based recommendation and ML-based techniques in a hybrid system is thus a natural and promising method to enhance semantic knowledge with statistical models. This merge could alleviate the burden of creating large, narrowly focused ontologies for complicated domains, by using probabilistic or generative models to enhance the predictions without attempting to provide a semantic support for them. In this paper, we present a novel hybrid recommendation system that blends a single architecture of classical knowledge-driven recommendations arising from a tailored ontology with recommendations generated by a data-driven approach, specifically with classifiers and a neural collaborative filtering. We show that bringing together these knowledge-driven and data-driven worlds provides some measurable improvement, enabling the transfer of semantic information to ML and, in the opposite direction, statistical knowledge to the ontology. Moreover, the novel proposed system enables the extraction of the reasoning recommendation results after updating the standard ontology with the new products and user behaviors, thus capturing the dynamic behavior of the environment of our interest.https://www.mdpi.com/1424-8220/22/2/700recommendation systemsneural collaborative filteringontologiesdeep learningretail dataset |
| spellingShingle | Rana Alaa El-deen Ahmed Manuel Fernández-Veiga Mariam Gawich Neural Collaborative Filtering with Ontologies for Integrated Recommendation Systems Sensors recommendation systems neural collaborative filtering ontologies deep learning retail dataset |
| title | Neural Collaborative Filtering with Ontologies for Integrated Recommendation Systems |
| title_full | Neural Collaborative Filtering with Ontologies for Integrated Recommendation Systems |
| title_fullStr | Neural Collaborative Filtering with Ontologies for Integrated Recommendation Systems |
| title_full_unstemmed | Neural Collaborative Filtering with Ontologies for Integrated Recommendation Systems |
| title_short | Neural Collaborative Filtering with Ontologies for Integrated Recommendation Systems |
| title_sort | neural collaborative filtering with ontologies for integrated recommendation systems |
| topic | recommendation systems neural collaborative filtering ontologies deep learning retail dataset |
| url | https://www.mdpi.com/1424-8220/22/2/700 |
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