The fuzzy cognitive pairwise comparisons for ranking and grade clustering to build a recommender system: An application of smartphone recommendation. (May 2017) - Yuen, Kevin Kam Fung
Abstract: In a competitive high-end product market, many enterprises offer a variety of products to compete the market shares in different segments. Due to rich information of plenty of competitive product alternatives, consumers face the challenges to compare and choose the most suitable products. Whilst a product comprises different tangible and intangible features, consumers tend to buy the features rather than a product itself. A successful product has most features meeting the consumer needs. Perception values of product features from consumers are complex to be measured and predicted. To reduce information overload for searching their preferred products, this paper proposes the Fuzzy Cognitive Pairwise Comparison for Ranking and Grading Clustering (FCPC-RGC) to build a recommender system. The fuzzy number enables rating flexibility for the users to handle rating uncertainty. The Fuzzy Cognitive Pairwise Comparison (FCPC) is used to evaluate consumer preferences for multiple features of a product by pairwise comparison ratings. The Fuzzy Grade Clustering (FGC) is used to group the product alternatives into different consumer preference grades. To verify the validity and applicability of FCPC-RGC, a smartphone recommender system using the proposal approach is demonstrated how the system is able to help the consumers to recommend the suitable products according to the customers' individual preference.