Author

  • B. A. Ojokoh

    • Department of Computer Science, Federal University of Technology, Akure, Nigeria
    • Nigeria

  • O. C. Olayemi

    • Joseph Ayo Babalola University, Ikeji-Arakeji, Nigeria
    • Nigeria

  • A. E. Babalola

    • Department of Mathematical Sciences, Anchor University, Lagos, Nigeria
    • Nigeria

  • E. O. Eyo

    • Department of Computer Science, Federal University of Technology, Akure, Nigeria
    • Nigeria

Abstract

Recommender systems are very useful in assisting users to reduce the complexities involved in their decision making processes. It is particularly difficult for people to make decisions on housing choices because different options exist with different facilities, in different locations and with varied cost implications. This paper proposes a hybrid user-centric housing recommender system that is implemented to assist potential house buyers and tenants to generate house listings based on their preferences with the aid of fuzzy logic and item-based collaborative filtering. A virtual tour of the houses is also provided for better choice making.

Keywords

References

  • Aggarwal, C. C. (2016). Recommender systems. Cham: Springer International Publishing, pp. 1-28.
  • Burke, R. (2001). Knowledge-based recommender systems. Encyclopedia of library and Information Science.
  • Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User modelling and user-adapted interaction, 12(4), 331-370.
  • Carpenter, S. R., Pingali, P. L., Bennett, E. M. & Zurek, M. B. (2005). Ecosystems and Human well-being: scenarios, volume 2.
  • Daly, E. M., Botea, A., Kishimoto, A. & Marinescu, R. (2014). Multi-criteria journey aware housing recommender system. Proceedings of the 8th ACM Conference on Recommender systems, 325-328.
  • Ikejiaku, B. V. (2009). The relationship between poverty, conflict and development. Journal of Sustainable Development, 2(1), 15.
  • Isinkaye, F., Folajimi, Y. & Ojokoh, B. (2015). Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal. 16. 10.1016/j.eij.2015.06.005.
  • Jain, A. & Gupta, C. (2018). Fuzzy Logic in Recommender Systems. In Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications (pp. 255-273). Springer, Cham.
  • Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R. & Riedl, J. (1997). Group Lens: applying collaborative filtering to Usenet news. Communications of the ACM, 40(3), 77-87.
  • Konstan, J., Miller, B., Maltz, D., Herlocker, J., Gordon, L. & Riedl, J. (1997). Group Lens: Applying Collaborative
  • Lorenzi, F., Fontanella, B., Prestes, E. & Peres, A. (2014). How to Improve Multi-Agent Recommendations Using Data from Social Networks? In FLAIRS Conference.
  • Lops, P., De Gemmis, M. & Semeraro, G. (2011). Content-based recommender systems: State of the art and trends. In Recommender systems handbook (pp. 73-105). Springer US.
  • Omisore, M. O. & Samuel, O. W. (2014). Personalized Recommender System for Digital Libraries. International Journal of Web-Based Learning and Teaching Technologies, 9(1), 18-32.
  • Ojokoh, B., Omisore, M., Samuel, O. & Ogunniyi, T. (2012). A fuzzy logic based personalized recommender system. International Journal of Computer Science and Information Technology and Security (IJCSITS), 2, 1008-1015.
  • Omisore, M. O., Samuel, O. W. & Ogunniyi, T. O. (2013). A Multi- Tecnique Approach for recommender Systems. International Journal of Advanced trends in Computer Science and Engineering, 2(5), 112-118.
  • Pan C, Li, W. (2010). Research paper recommendation with topic analysis. In Computer Design and Applications IEEE, 4, 4-264.
  • Pathak, B., Garfinkel, R., Gopal, R., Venkatesan, R. & Yin, F. (2010). Empirical analysis of the impact of recommender systems on sales. Manage Inform System, 27(2), 159–88.
  • Pizzato, L., Rej, T., Chung, T., Koprinska, I. & Kay, J. (2010). RECON: a reciprocal recommender for online dating. In Proceedings of the fourth ACM conference on Recommender systems, 207-214.
  • Sarwar, B., Karypis, G., Konstan, G. & Riedl, J. (2001). Item-based Collaborative Filtering Recommendation Algorithms, WWW10, May 1-5, 2001, Hong Kong.
  • Schafer, J. B., Konstan, J. & Riedl, J. (1999). Recommender systems in e-commerce. In Proceedings of the 1st ACM conference on Electronic commerce, 158-166.
  • Schafer, J. B., Konstan, J. A. & Riedl, J. (2002). Meta-Recommendation Systems: User-Controlled Integration of Diverse Recommendations. Proceedings of the Eleventh International Conference on Information and Knowledge Management (2002) ACM Press: McLean, Virginia, USA: 43-51.
  • Shanmuganathan, M. & Karthikeyan, R. (2016). A Web-based Recommendation System for Housing Selection: Design, Implementation & Evaluation. Data Mining and Knowledge Engineering, 8(5), 134-137.
  • Terveen, L., Hill, W., Amento, B., McDonald, D. & Creter, J. (1997). PHOAKS: A System for Sharing Recommendations. Communications of the ACM, 40(3), 59-62.
  • Yuan, X., Lee, J. H., Kim, S. J. & Kim, Y. H. (2013). Toward a user-oriented recommendation system for real estate websites. Information Systems, 38(2), 231-243.

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How to Cite

Ojokoh, B. A., Olayemi, O. C., Babalola, A. E., & Eyo, E. O. (2018). A User-Centric Housing Recommender System. Information Management and Business Review, 10(3), 17-24. https://doi.org/10.22610/imbr.v10i3.2460

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Copyright (c) 2018 B. A. Ojokoh, O. C. Olayemi, A. E. Babalola, E. O. Eyo (Author)

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Section

Research Paper

Published in

Vol. 10 No. 3 (2018) Strategic HR, Recommender Systems & Microfinance View issue contents

Published

2018-10-13

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