A Well-Built Hybrid Recommender System for Agricultural Products in Benue State of Nigeria


Benue State of Nigeria is tagged the Food Basket of the country due to its heavy production of many classes of food. Situated in the North Central Geo-Political area of the country, its food production ranges from root crops, fruits to cereals. Recommender systems (RSs) allow users to access products of interest, given a plethora of interest on the Internet. Recommendation techniques are content-based and collaborative filtering. Recommender systems based on collaborative filtering outshines content-based systems in the quality of their recommendations, but suffers from the cold start problem, i.e., not being able to recommend items that have few or no ratings. On the other hand, content-based recommender systems are able to recommend both old and new items but with low recommendation quality in relation to the user’s preference. This work combines collaborative filtering and content based recommendation into one system and presents experimental results obtained from a web and mobile application used in the simulation. The work solves the problem of serendipity associated with content based (RS) as well as the problem of ramp-up associated with collaborative filtering. The results indicate that the quality of recommendation is promising and is competitive with collaborative technique recommending items that have been seen before and also effective at recommending cold-start products.

Share and Cite:

Iorshase, A. and Charles, O. (2015) A Well-Built Hybrid Recommender System for Agricultural Products in Benue State of Nigeria. Journal of Software Engineering and Applications, 8, 581-589. doi: 10.4236/jsea.2015.811055.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Adomavicius, G. and Tuzhilin, A. (2005) Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering, 17, 734-749.
[2] Burke, R. (2002) Hybrid Recommender Systems: Survey and Experiments. User Modelling and User-Adapted Interaction, 12, 331-370.
[3] Herlocker, J.L., Konstan, J.A., Borchers, A. and Riedl, J. (1999) An Algorithmic Framework for Performing Collaborative Filtering. Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 230-237.
[4] Pazzani, M. and Billsus, D. (1997) Learning and Revising User Profiles: The Identification of Interesting Web Sites. Machine Learning: Special Issue on Multistrategy Learning, 27, 313-331.
[5] Ujwala, H.W., Sheetal, R.V. and Debajyoti, M. (2013) A Hybrid Web Recommendation System Based on the Improved Association Rule Mining Algorithm. Journal of Software Engineering and Applications, 6, 396-404.
[6] Pagare, R. and Shinde, A. (2013) Recommendation System Using Bloom Filter in Map Reduce. International Journal of Data Mining and Knowledge Management Process (IJDKP), 3, 127-134.
[7] Monteiro, E., Valante, F., Costa, C. and Oliveira, J.L. (2015) A Recommendation System for Medical Imaging Diagnostic. Studies in Health Technology and Informatics, 210, 461-463.
[8] Jayshri, M.S. and Gurav, Y.B. (2014) Cloud-Based Mobile Multinedia recommendation System with User Behavior Information. International Journal of Innovative Research in Computer Science and Communication, 2, 6830-6834.
[9] Aher, S.B and Labo, L.M.R.J. (2012) Course Recommender System in E-Learning. International Journal of Computer Science and Communication, 3, 159-164.
[10] Ye, M., Tang, Z., Xu, J.B. and Jin, L.F. (2015) Recommender System for E-Learning Based on Semantic Relatedness of Concepts. Information, 6, 443-453.
[11] Bart, P.K., Martijn, C.W., Zeno, G., Hakan, S. and Chris, W. (2012) Explaining the User Experience of Recommender Systems. User Modeling and User-Adapted Interaction, 22, 441-504.
[12] Ahmed, M.O. and Motaz, K. (2013) An Intelligent Recommender System for Long View of Egypt’s Livestock Production. AASRI Procedia, 6, 103-110.

Copyright © 2022 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.