A Web-based Recommendation System for Housing Selection: Design, Implementation & Evaluation
Abstract
Recommendation systems provide appropriate solutions to the users to reduce their decision complexity. This has become very popular today in the Internet World. The design and evaluation of such systems are the essential challenges for the researcher and online professionals. But the critical task is how to obtain the user preferences. This paper focuses on the recommendation services for flats availability within Chennai city limits and its surroundings (both urban and rural areas), and how the people from Chennai city choose a flat. The introduction of different flat (options) with different modern facilities, different areas, variety of amenities, and different budgets have made consumer’s decision making more complex. Online personalized recommendation systems help to improve consumer satisfaction. Usually a recommendation system is considered to be a success if the consumer buys the recommended products. But the act of purchasing itself does not guarantee satisfaction, and a truly successful recommendation system should be one that maximizes the customer’s after-use satisfaction. Employing an innovative MultiCriteria Decision Making technique (MCDM), such as the Analytical Hierarchy Process (AHP) lays the foundation for supporting complex product comparisons and evaluation of consumers. In this paper, we demonstrate the approach of the AHP method to develop a web-based recommendation system and experimentally evaluate the system by 111 participants. All the participants are internet users. This paper focuses on the simplicity and effectiveness of the AHP algorithm and system satisfaction. This systematic study contributes to research, and practically shows how the recommendation systems helps the consumers in reducing the decision complexities and the cost (by avoiding brokerage charges), introducing a variety of products in particular and improving the strategy of business. Based on the consumer’s behavior, a product will be recommended to the prospective buyer if our model predicts his/ her satisfaction level as high. The achievability of the proposed recommendation system is validated through the system. The paper includes: I.Introduction, II. Need for the Recommendor System, III. Objective of the Experiment, IV. An Overview of AHP, V. Architectural Design of System Implemenation, VI. Experimental Evaluation of the AHP based System, VII. Experiments Results and Discussion, VIII. Conclusion and References.
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Ahmad,N, PA Laplante (2009), Using the analytical hierarchy process in selecting commercial real-time operating systems, International Journal of Information Technology & Decision Making Volume 08, Issue 01, World Scientific Publishing Co.
Amy N. Langville and Carl Dean Meyer, (2012). Who's # 1? : The Science of Rating and Ranking .Princeton University Press, p. 51.
Belton, V. and Gear, T. (1983) ‘On a shortcoming of Saaty’s method of analytic hierarchies’, Omega, 11, 228-230.
A.V.Bodapati, Recommendation Systems with purchase data. Journal of Marketing Research (JMR 45(1)) (2008) 77-93.
Garg, P, A. Gupta, and J.W. Rozenblit , (2004). Performance Analysis of Embedded Systems in the Virtual Component Co-Design Environment. Proceedings of the 11th IEEE International Conference and Workshop on the Engineering of Computer-Based Systems (ECBS’04) .2004 IEEE.
J.L.Herlocker, J.A.Konston, J.Loren, G.Terveen, T.Riedl, Evaluating Collaborative filtering recommender systems, ACM transaction on information systems 22(1) (2004) 5-53.
Jyrki Kontio1, (1996).A Case Study in Applying a Systematic Method for COTS Selection Copyright 1996 IEEE. Published in the Proceedings of the 18th International Conference on Software Engineering (ICSE-18), March 25-29, Berlin, Germany.
T.P.Liang, Y.F.Yang, D.N.Chen, Y.C.Ku, A semantic – expansion approach to personalized knowledge recommendation, DSS 45(3)(2008) 401 – 412.
Hamed Maleki, Sajjad Zahir, (2012). A Comprehensive Literature Review of the Rank Reversal Phenomenon in the Analytic Hierarchy Process. J. Multi-Crit. Decis. Anal. .
Matthew J. Liberatore , Robert L. Nydick,(2008). The analytic hierarchy process in medical and health care decision making: A literature review .European Journal of Operational Research 189 (2008) 194–207.
]Omkarprasad S. Vaidya , Sushil Kumar,(2006). Analytic hierarchy process: An overview of applications,European Journal of Operational Research 169 .pp. 1–29 ,
S.Senecal, J.Nantel, The influence of online product recommendation on consumers online choices, Journal of Retailing 80(2) (2004)159-169.
Thomas L Saaty, LG Vargas, (2012). Infertility Decision Making - Chapter 23 in Models, Methods, Concepts & Applications of the Analytic Hierarchy Process,p 323-330 , Springer.
Thomas L Saaty, LG Vargas (2012).Deciding between angioplasty and coronary artery bypass surgery - Chapter 24 in Models, Methods, Concepts & Applications of the Analytic Hierarchy Process,p 331-341 , Springer.
Thomas L Saaty, H Gholamnezhad, (1982). High-level nuclear waste management: analysis of options , Environment and Planning B, 1982
Tom Koch,(1996). Normative and Prescriptive Criteria: The Efficacy Of Organ Transplantation Allocation Protocols..Theoretical Medicine 17:75-93, 1996.
Vidal, Ludovic- Alexandre, Sahin, Evren; Martelli, Nicolas; Berhoune, Malik; Bonam,Brigitte (2010). Applying AHP to select drugs to be produced by anticipation in a chemotherapy Compounding unit. Expert System with Applications, Vol – 37, issue 2, pp.1528 – 1534.
A.Zenebe, A.F.Norcio, Representation, similarity measures and aggregation methods using fuzzy sets for content-based recommender systems. Fuzzy sets and systems 160(1)(2009)76-94.
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