Using the Nearest Neighbor Algorithm to Build a Web-Based Recommender System for House Trading and Matching

Abstract:

This paper is to present one of the techniques of data mining called the nearest neighbor algorithm to build a web-based recommender system for house trading and matching. The nearest neighbor algorithm is a pragmatic and highly accurate algorithm and can be used in the recommender system. Its use could enhance the systems matching ability so that users can easily obtain the information they need. This system offers a better way to get beyond the disadvantages of the traditional conditional or Boolean method that real estate agencies use to search for appropriate houses. Through improving the traditional but inefficient approach to searching, users will not waste much time without getting useful information or good offers of real estate.

• Periodical: Advanced Materials Research (Volume 811)
• Pages: 564-568
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.811.564
• Online since: September 2013
• Authors: Chung C. Chang
• Keywords: Data Mining (DM), Decision Support System (DSS), House Trading, Matching, Nearest Neighbor Algorithm, Recommender System

References

[1] J. Han and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, San Francisco, CA (2001).

[2] M. J. A. Berry and G. Linoff, Data Mining Techniques: for Marketing, Sales, and Customer Support, John Wiley & Sons, Inc., New York, NY (1997).

[3] C. WestPhal and T. Blaxton, Data Mining Solutions: Methods and Tools for Solving Real-World Problems, John Wiley & Sons, Inc., New York, NY (1998).

[4] B. Yan, and C. Domeniconi, Nearest Neighbor Ensemble, Proceedings of ICPR04 (2004), pp.228-231.

[5] W. J. Frawley, G. Piatetsky-Shapiro, and C. J. Matheus, Knowledge Discovery in Database, MIT Press, Cambridge, MA (1991).

[6] M. A. Ghazanfar and A. Prugel-Bennett, A Scalable, Accurate Hybrid Recommender System, Proceedings of The Third International Conference on Knowledge Discovery and Data Mining (2010), pp.94-98.

DOI: 10.1109/wkdd.2010.117

[7] S. Gong, H. W. Ye and H. S. Tang, Combining Memory-Based and Model-Based Collaborative Filtering in Recommendation System, Proceedings of Pacific-Asia Conference on Circuit, Communication and System (2009), pp.690-693.

DOI: 10.1109/paccs.2009.66

[8] L. G. Perez, B. Montes-Berges and M. R. Castillo-Mayen, Boosting Social Networks in Social Network-Based Recommender System, Proceedings of The 11th International Conference on Intelligent Systems Design and Applications (2011), pp.426-431.

DOI: 10.1109/isda.2011.6121693

[9] Q. Yang, J. Sun, J. Wang and Z. Jin, Semantic Web-Based Personalized Recommendation System of Courses Knowledge Research, Proceedings of International Conference on Intelligent Computing and Cognitive Informatics (2010), pp.214-207.

DOI: 10.1109/icicci.2010.54

[10] Z. Mu, L. Jing, C. Shan and F. Lei, Design of the Tourism-Information-Service-Oriented Collaborative Filtering Recommendation Algorithm, Proceedings of International Conference on Computer Application and System Modeling (2010), pp.361-365.

DOI: 10.1109/iccasm.2010.5622799