Purpose
The purpose of this paper is to improve the customer satisfaction by offering online personalized recommendation system.
Design/methodology/approach
By employing an innovative associative classification method, this paper is able to predict a customer’s pleasure during the online while-recommending process. Consumers can make an active decision to recommended products. Based on customer’s characteristics, a product will be recommended to the potential buyer if the model predicts that he/she will click to view the product. That is, he/she is satisfied with the recommended product. Finally, the feasibility of the proposed recommendation system is validated through a Taobao shop.
Findings
The results of the experimental study clearly show that the online personalized recommendation system maximizes the customer’s satisfaction during the online while-recommending process based on an innovative associative classification method on the basis of consumer initiative decision.
Originality/value
Conventionally, customers are considered as passive recipients of the recommendation system. However, customers are tired of the recommendation system, and they can do nothing sometimes. This paper designs a new recommendation system on the basis of consumer initiative decision. The proposed recommendation system maximizes the customer’s satisfaction during the online while-recommending process.
A semi-supervised associative classification method for POS tagging
