Ontology-Based E-Commerce Recommender System: A Hybrid Semantic Filtering Approach
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Abstract
The rapid growth of e-commerce has led to product overload, which has resulted in personalized product discovery becoming a crucial problem for consumers. Classic recommender systems (RS), which rely heavily on content-based filtering or collaborative filtering, tend to face well-known problems such as cold start, data sparsity, and ignorance of semantics. Such constraints frequently result in irrelevant or redundant suggestions, thereby lowering the user satisfaction and conversions. This study presents a hybrid ontology-based e-commerce recommender system that combines symbolic reasoning with deep semantic matching. The system is based on a Neo4j graph database to capture structured product relationships and is combined with sentence embedding models (MiniLM) to compute the semantic similarity between user queries and product data. For semantic matching, cosine similarity is used, and for ontology-based filtering, graph relationships, such as SAME_CATEGORY, SIMILAR_PRICE, and SAME_MANUFACTURER, are employed. An e-commerce dataset that was cleaned and pre-processed was used to test the system. The performance was measured using the following metrics: precision, Recall, F1-Score, and accuracy. The performance was measured using the following metrics: precision, Recall, F1-Score, and accuracy. The proposed system achieved a precision of 0.95, recall of 0.93, F1-Score of 0.94, and accuracy of 0.94, demonstrating that the hybrid approach yields superior recommendation quality compared with using a single method.
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References
N.M.S. Chelvam, S. Haw, L. Krisnawati, and A. Mahastama, “Hybrid-based recommender system based on electronic product reviews”, JOIV : International Journal on Informatics Visualization, vol. 9, no. 4, pp. 1752, 2025, doi: 10.62527/joiv.9.4.3561.
M. Shili, and O. Sohaib, “Geographic recommender systems in e-commerce based on population”, PeerJ Computer Science, vol. 11, pp. e2525, 2025, doi: 10.7717/peerj-cs.2525.
S. Cahya, and Z. Baizal, “Personalized recommender system for e-commerce grocery using time-based collaborative filtering”, 2025 International Conference on Advancement in Data Science, E-Learning and Information System (ICADEIS), pp. 1-6, 2025, doi: 10.1109/icadeis65852.2025.10933467.
R. Buettner, “Predicting user behavior in electronic markets based on personality-mining in large online social networks”, Electronic Markets, vol. 27, no. 3, pp. 247-265, 2016, doi: 10.1007/s12525-016-0228-z.
E. Anaam, S. Haw, and P. Naveen, “Applied fuzzy and analytic hierarchy process in hybrid recommendation approaches for e-CRM”, JOIV : International Journal on Informatics Visualization, vol. 6, no. 2-2, pp. 553, 2022, doi: 10.30630/joiv.6.2-2.1043.
A. Bhatti, H. Akram, H. M. Basit, and A. U. Khan, “E-commerce trends during COVID-19 pandemic,” International Journal of Future Generation Communication and Networking, vol. 13, no. 2, 2020.
B. Wang, and X. Zhang, “Personalized recommendation of e-commerce based on big data technology”, Procedia Computer Science, vol. 262, pp. 1115-1122, 2025, doi: 10.1016/j.procs.2025.05.149.
P. Alamdari, N. Navimipour, M. Hosseinzadeh, A. Safaei, and A. Darwesh, “A systematic study on the recommender systems in the e-commerce”, IEEE Access, vol. 8, pp. 115694-115716, 2020, doi: 10.1109/access.2020.3002803.
W.-E. Kong, T.-E. Tai, P. Naveen, and H. A. Santoso, “Performance evaluation on e-commerce recommender system based on KNN, SVD, coclustering and ensemble approaches”, Journal of Informatics and Web Engineering, vol. 3, no. 3, pp. 63-76, 2024, doi: 10.33093/jiwe.2024.3.3.4.
J. Zhang, M. Balaji, J. Luo, and S. Jha, “Effectiveness of product recommendation framing on online retail platforms”, Journal of Business Research, vol. 153, pp. 185-197, 2022, doi: 10.1016/j.jbusres.2022.08.006.
K. Keerthika, and T. Saravanan, “Enhanced product recommendations based on seasonality and demography in ecommerce”, 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), pp. 721-723, 2020, doi: 10.1109/icacccn51052.2020.9362760.
Z. Fayyaz, M. Ebrahimian, D. Nawara, A. Ibrahim, and R. Kashef, “Recommendation systems: Algorithms, challenges, metrics, and business opportunities”, Applied Sciences, vol. 10, no. 21, pp. 7748, 2020, doi: 10.3390/app10217748.
F. Isinkaye, Y. Folajimi, and B. Ojokoh, “Recommendation systems: principles, methods and evaluation”, Egyptian Informatics Journal, vol. 16, no. 3, pp. 261-273, 2015, doi: 10.1016/j.eij.2015.06.005.
S. Raza et al., “A comprehensive review of recommender systems: Transitioning from theory to practice,” arXiv Preprint, Jul. 2024, doi: 10.48550/arXiv.2407.13699.
F. Shang, F. Zhao, M. Zhang, u. Sun, and J. Shi, “Personalized recommendation systems powered by Large Language Models: Integrating semantic understanding and user preferences”, International Journal of Innovative Research in Engineering and Management, vol. 11, no. 4, pp. 39-49, 2024, doi: 10.55524/ijirem.2024.11.4.6.
S. Zhang, L. Yao, A. Sun, and Y. Tay, “Deep learning based recommender system”, ACM Computing Surveys, vol. 52, no. 1, pp. 1-38, 2019, doi: 10.1145/3285029.
L. J. Chew, S. C. Haw, S. Subramaniam, and K. W. Ng, “A hybrid ontology-based recommender system utilizing data enrichment and SVD approaches,” Journal of System and Management Sciences, vol. 12, no. 5, pp. 1–20, 2022, doi: 10.33168/JSMS.2022.0509.
S. C. Haw, K. W. Ng, J. Jayapradha, and P. Naveen, “OD-SIF: An ontology-driven schema integration framework for e-commerce platform”, 2024 7th Asia Conference on Cognitive Engineering and Intelligent Lnteraction (CEII), pp. 196-201, 2024, doi: 10.1109/ceii65291.2024.00046.
U. Javed, K. Shaukat, I. Hameed, F. Iqbal, T. Alam, and S. Luo, “A review of content-based and context-based recommendation systems”, International Journal of Emerging Technologies in Learning (IJET), vol. 16, no. 03, pp. 274, 2021, doi: 10.3991/ijet.v16i03.18851.
T. Wang, “The ontology recommendation system in e-commerce based on data mining and web mining technology”, Advances in Intelligent and Soft Computing, pp. 533-536, 2012, doi: 10.1007/978-3-642-28658-2_83.
R. Karthik, and S. Ganapathy, “A fuzzy recommendation system for predicting the customers interests using sentiment analysis and ontology in e-commerce”, Applied Soft Computing, vol. 108, pp. 107396, 2021, doi: 10.1016/j.asoc.2021.107396.
G. Mustafa et al., “OntoCommerce: Incorporating ontology and sequential pattern mining for personalized e-commerce recommendations," in IEEE Access, vol. 12, pp. 42329-42342, 2024, doi: 10.1109/ACCESS.2024.3377120.
A. Subramanian, A. Mohan, S. M. Prabin, and D. Ajitha, “Mitigating cold start in recommender systems: A deep learning approach integrating ARM and filtering techniques,” AIP Conference Proceedings, vol. 3449, p. 020240, 2026, doi: https://doi.org/10.1063/5.0299767.
B. Asri, S. Qassimi, and S. Rakrak, “A Review of Advances in Adaptive Personalized Recommender Systems,” Lecture Notes in Networks and Systems, pp. 185–207, Nov. 2025, doi: https://doi.org/10.1007/978-3-032-02312-4_15.
Bihi Sabiri, A. Khtira, Bouchra EL Asri, and Maryem Rhanoui, “Hybrid Quality-Based Recommender Systems: A Systematic Literature Review,” Journal of Imaging, vol. 11, no. 1, pp. 12–12, Jan. 2025, doi: https://doi.org/10.3390/jimaging11010012.
R. Garapati and M. Chakraborty, “Recommender systems in the digital age: a comprehensive review of methods, challenges, and applications,” Knowledge and Information Systems, vol. 67, no. 8, pp. 6367–6411, May 2025, doi: 10.1007/s10115-025-02453-y.