A Novel Process of Group-oriented Question Reduction for Rulebased Recommendation Websites

Chen, L., Emerson, D. and Nayak, R.

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.

Cite as: Chen, L., Emerson, D. and Nayak, R. (2013). A Novel Process of Group-oriented Question Reduction for Rulebased Recommendation Websites. In Proc. Data Mining and Analytics 2013 (AusDM'13) Canberra, Australia. CRPIT, 146. Christen, P., Kennedy, P., Liu, L., Ong, K.L., Stranieri, A. and Zhao, Y. Eds., ACS. 173-180

(from crpit.com) (local if available)