A novel process of group-oriented question reduction for rule-based recommendation websites
Contribuinte(s) |
Christen, Peter Kennedy, Paul |
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Data(s) |
24/03/2014
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Resumo |
Several websites utilise a rule-base recommendation system, which generates choices based on a series of questionnaires, for recommending products to users. This approach has a high risk of customer attrition and the bottleneck is the questionnaire set. If the questioning process is too long, complex or tedious; users are most likely to quit the questionnaire before a product is recommended to them. If the questioning process is short; the user intensions cannot be gathered. The commonly used feature selection methods do not provide a satisfactory solution. We propose a novel process combining clustering, decisions tree and association rule mining for a group-oriented question reduction process. The question set is reduced according to common properties that are shared by a specific group of users. When applied on a real-world website, the proposed combined method outperforms the methods where the reduction of question is done only by using association rule mining or only by observing distribution within the group. |
Formato |
application/pdf |
Identificador | |
Publicador |
Australian Computer Society Inc. |
Relação |
http://eprints.qut.edu.au/69090/1/AusDMIndustryFinal_Version1%5B2%5D.pdf http://togaware.com/access/crpit_vol146_ausdm2013.pdf Chen, Lin, Emerson, Daniel, & Nayak, Richi (2014) A novel process of group-oriented question reduction for rule-based recommendation websites. In Christen, Peter & Kennedy, Paul (Eds.) Data Mining and Analytics 2013: Proceedings of the 11th Australasian Data Mining Conference, AusDM 2013 [Conferences in Research and Practice in Information Technology, Volume 146], Australian Computer Society Inc., Australian National University, Canberra, ACT, pp. 173-180. |
Direitos |
Copyright 2013, Australian Computer Society, Inc. This paper appeared at the Eleventh Australasian Data Mining Conference (AusDM 2013), Canberra, 13-15 November 2013. Conferences in Research and Practice in Information Technology (CRPIT), Vol. 146. Peter Christen, Paul Kennedy, Lin Liu, Kok-Leong Ong, Andrew Stranieri and Yanchang Zhao, Eds. Reproduction for academic, not-for-profit purposes permitted provided this text is included. |
Fonte |
School of Electrical Engineering & Computer Science; Science & Engineering Faculty |
Palavras-Chave | #080109 Pattern Recognition and Data Mining #Question Reduction #Clustering #Classification |
Tipo |
Conference Paper |