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An Enhanced Hybrid Item Recommender Model for Nigerian Online Stores

A.T. Olaniran, I.O. Awoyelu, A.O. Amoo, B.O. Akinyemi, R.O. Abimbola. Published in Information Sciences

International Journal of Applied Information Systems
Year of Publication: 2015
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: A.T. Olaniran, I.O. Awoyelu, A.O. Amoo, B.O. Akinyemi, R.O. Abimbola
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  1. A T Olaniran, I O Awoyelu, A O Amoo, B O Akinyemi and R O Abimbola. Article: An Enhanced Hybrid Item Recommender Model for Nigerian Online Stores. International Journal of Applied Information Systems 10(1):31-42, November 2015. BibTeX

    	author = "A.T. Olaniran and I.O. Awoyelu and A.O. Amoo and B.O. Akinyemi and R.O. Abimbola",
    	title = "Article: An Enhanced Hybrid Item Recommender Model for Nigerian Online Stores",
    	journal = "International Journal of Applied Information Systems",
    	year = 2015,
    	volume = 10,
    	number = 1,
    	pages = "31-42",
    	month = "November",
    	note = "Published by Foundation of Computer Science (FCS), NY, USA"


Item recommendation is the process of recommending goods sold on online stores to visitors and existing customers of the store to aid their shopping transactions processes. Majority of the online stores in Nigeria have their shopping systems implemented similar to foreign online stores’ templates. Adapting these foreign shopping system templates to meeting the needs of Nigerian consumers has been quite challenging. This is due to the unavailability and sparsity of ratings needed by the systems for the generation of these recommendations, thus Nigerian online stores focus on the provision of non-personalized recommendations. The peculiarities of Nigerian consumers call for the provision of personalized item recommendations using alternative information other than ratings information. A hybrid item recommender system that has been demographically enhanced is being proposed in this paper. The model was formulated using the search method, user profiling and association rule mining for the content-based item recommendations. The vector similarity and the adjusted cosine similarity methods were used for formulating the collaborative item recommendations. The demographic item recommendations were then formulated using the clustering and association rule methods.

The performance of the system was evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results of the performance evaluation carried out on the system showed that the system was able to reduce the Mean Absolute Error of the existing system by 61.24% and the Root Mean Square Error by 37.23% in content-based recommendations. In collaborative recommendations, evaluation results further showed that the new system was able to reduce the Mean Absolute Error of the existing system by 63.16% and the Root Mean Square Error by 39.30%.


  1. Bobadilla, J., Ortega, F. and Hernando, A. 2012. A Collaborative Filtering Similarity Measure based on Singularities. Journal of Information Processing and Management, 48(2): pp. 204-217.
  2. Chen, T., and He, L. 2009. Collaborative Filtering based on Demographic Attribute Vector. In International Conference on Future Computer and Communication (FCC'09) (pp.225-229), Canada.
  3. Chikhaoui, B., Chiazzaro, M. and Wang, S. 2011. An Improved Hybrid Recommender System by Combining Predictions. In Advanced Information Networking and Applications (WAINA) 2011 IEEE Workshops (pp. 644-649), Biopolis.
  4. Chu, W. and Park, S. T. 2009. Personalized Recommendations on Dynamic Content Using Predictive Bilinear Models. In Proceedings of the 18th International Conference on World Wide Web (pp. 691-700), Madrid, Spain.
  5. Deepa, R. and Hamsaveni, R. 2010. Online Customer Value Identification Based on Site Usage Time through Data Mining Analysis. Global Journal of Computer Science and Technology, 10(2): pp. 10-16.
  6. Deshpande, M. and Karypis, G. 2004. Item-based Top-N Recommendation Algorithms. ACM Transactions on Information Systems (TOIS), 22(1): pp. 143-177.
  7. Edson B., Santos, J., Marecelo, G. M. and Rudinei, C.2015. Evaluating the Impact of Demographic Data on a Hybrid Recommender Model. IADIS International Journal on WWW/Internet, 12(8): pp. 14-21.
  8. Escriche, M. and Symeon, P. 2011. User Profiling and Personalization Tools. ‘WeKnowIt’ Emerging, Collective Intelligence for Personal, Organizational, and Social Use Journal, 1(2): pp. 1-26.
  9. Jafari, M., Sabzchi, F. S. and Irani, A. J. 2014. Applying Web Usage Mining Techniques to Design Effective Web Recommendation Systems: A Case Study. Journal of Advances in Computer Science, 3(2): pp. 78-90.
  10. Kabore, S. C. 2012. Design and Implementation of a Recommender System as a Module for Liferay Portal. In Barcelona School of Computing (FIB), University Polytechnic of Catalunya (UPC) (pp. 1-127), Barcelona, Spain.
  11. Mabude, C., N. 2014. Development of an Improved Model for Expertise Recommendation in Academic Research. Unpublished M.Sc Thesis Submitted to the Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife. 2014; pp. 1-129.
  12. Prassad, R. and Kumari, V. V. 2012. A Categorical Review of Recommender Systems. International Journal of Distributed and Parallel Systems (IJDPS), 3(5): pp. 73-83.
  13. Ristoski, P., Mencía, E. L. and Paulheim, H. 2014. A Hybrid Multi-Strategy Recommender System Using Linked Open Data. Semantic Web Evaluation Challenge, 1(2): pp. 150-156.
  14. Safoury, L. and Salah, A. 2013. Exploiting User Demographic Attributes for Solving Cold-Start Problem in Recommender System. Lecture Notes on Software Engineering, 1(3): pp. 303-307.
  15. Sharma, S. K. and Suman, U. 2011. Design and Implementation of Architectural Framework of Recommender System for E-commerce. International Journal of Computer Science and Information Technology & Security (IJCSITS), 1(2): pp. 153-162.
  16. Vozalis, M. and Margaritis, K. G. 2004. Enhancing Collaborative Filtering with Demographic Data: The Case of Item-based Filtering. In Proceedings of the Fourth IEEE International Conference on Intelligent Systems Designs and Application (pp. 1-6), Brazil.
  17. Zenebe, A., Ozok, A. and Norcio, A. F. 2005. Personalized Recommender Systems in E-Commerce and M-Commerce: A Comparative Study. Research Gate publications, 1(2): pp. 1-10.


Purchase data, Demographic data, Item recommendation, Online Shopping