Recommendation system is an important method of solving the problem of information overload. It also helps consumers to save time while searching for goods. Numerous recommendation techniques are proposed. However, they still have to confront some weaknesses such as cold-start, gray sheep and matrix sparsity problems. The purpose of this paper is to propose a method to overcome the cold-start problem and recommend a fit item for consumers to improve the personalized service. The proposed method can be applied in the e-commerce websites of exclusive or specialty stores. It is a combination of the product knowledge and Analytic Hierarchy Process (AHP) method. There are two phases in the proposed method. Phase 1 is to calculate the weight between product attributes and create a candidate product set. Phase 2 is to conduct the recommendation from the candidate set. This paper also introduces the implementation experiences by taking the badminton racket recommendation as a case study example. ? 2011 IEEE.
Relation:
Proceedings - 2011 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2011 2011, Article number6079397, Pages 16-23
