AN ONTOLOGY BASED PRODUCT RECOMMENDATION SYSTEM FOR NEXT GENERATION E-RETAIL

ABSTRACT

The number of e-commerce resources has increased considerably. Thus, it has become important for sellers to be able to quickly recommend products to potential buyers. Some product recommendation systems developed for this purpose. However, due to the lack of semantics, the systems’ success in recommending accurate products according to user preferences is low. In this study carried out within the scope of a state-funded R&D project, an ontology-based personalized product recommendation system named E-Prod was developed. E-Prod tracks various e-commerce systems in real time and transfers the product information to the ontology model. E-Prod uses a novel recommendation approach that combines machine learning and semantic matching to provide personalized recommendations. The system learns user’s preferences based on semantic relationships between products by monitoring their behaviors. In this way, accurate recommendations are made by semantic matching between products and user preferences. E-Prod has been tested with over 250 registered users and compared to traditional collaborative recommendations in terms of accuracy, precision, and recall. As a result, E-Prod outperformed traditional methods by 92.79% accuracy, 92.93% precision, and 90.58% recall. Within the scope of this study, E-Prod covers the clothing, shoes, and bag retail sectors. However, it provides a generic infrastructure for new generation e-commerce systems. Its reusable modules can be adapted to any domain.

KEYWORDS:

• e-commerce
• ontology engineering
• machine learning
• ontology based machine learning
• semantic interpretation
• personal recommendation
• decision tree

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ The property that has the least effect on the user’s product preference.

Additional information

Funding

This work was supported by the Turkish Scientific and Technical Research Institute (TUBİTAK) under Grant TEYDEB Project ID: 7141011.

Notes on contributors

Ali Murat Tiryaki

Ali Murat Tiryaki is an assistant professor in the Department of Computer Engineering, Canakkale Onsekiz Mart University, Canakkale, Turkey. He received his Ph.D. degree in computer engineering from Ege University, İzmir, Turkey in 2009. His current research interests include recommendation systems, agile software development, semantic web, ontology engineering and machine learning. He teaches undergraduate and graduate courses in software engineering.

Sait Can Yücebaş

Sait Can Yücebaş is an assistant professor in the Department of Computer Engineering, Canakkale Onsekiz Mart University, Canakkale, Turkey. He received his PhD degree from Middle East Technical University (METU), Ankara, Turkey in 2013, is BS and MS degrees from Baskent University, Ankara, Turkey, in 2003 and 2006, respectively. His current research interests include machine learning, artificial intelligence, natural language processing, bio-informatics and medical informatic