A Retailing Recommender System Based on Customer's Demographic Features using SOM and Association Rules

The intensive competition in e-Commerce causes effective methods for customer attraction of special importance. In this regard, the recommender systems in commercial websites can precisely determine customer's interests and needs, and offer them most suitable products and services. In this paper, a new model for recommender systems is proposed which segments the market and customers more efficiently, and then provides customers with better offers in two phases; customers are segmented based on demographic features such as age, gender, occupation and education in the first phase. The number of clusters are determined by means of the self-organizing map (SOM), and then clusters are created using K-means. In the second phase, association rules determine a valid map for each cluster which yields the most suitable offers to customers. To examine the efficiency of the proposed model in practice, it is used in an Iranian commercial website, and the results are analyzed.