Collaborative Filtering Based Recommendation System for Product Bundling
Guorong Liu, Xizheng Zhang · 2006
Abstract: Due to the convenience of Internet, people can search for whatever information they need and buy whatever they want on the web. In the age of E-Commerce, it is important for firms to develop web-based marketing strategy such as product bundling to increase revenue. Recommendation system(RS) is a platform that can be used to reduce the searching cost of consumers, increase the effectiveness of firm's promotion strategies and enhance consumer's loyalty. This paper propose a web-based collaborative filtering mechanism for firm's product bundling strategy. First, Clustering using Adaptive Resonance Theory is applied to generate different kinds of customer group, in which members have same attributes or habits. Second, Data Mining using association rule techniques is applied to find the relations between two products in given support and confidence values. And last, the RS value is calculated to generate personal product bundling list and top-N recommendations. Experiment results also show that it is a feasible and effective design scheme.