Recommender system based on customer segmentation (RSCS)

Abstract

Purpose– Recommender systems are techniques that allow companies to develop sales and marketing and as a result, attract more customers. There are several different types of recommender systems which collaborative filtering (CF) method is more popular and is used in various fields. However, similar to other recommender systems, this system has its own limitations. Nowadays, recommender systems are combined with other systems to enhance the quality and precision. The purpose of this paper is to present a new method to increase the accuracy and quality of recommendations associated with filtering systems.Design/methodology/approach– First, the recency, frequency, and monetary (RFM) variables of the clients are extracted and variables’ weights are calculated. Then, using weighted RFM and expectation maximization clustering algorithms and their combination with the closest K-neighbors, recommendations for each cluster is independently extracted. Finally, the results are compared with the outcome of conventional CF techniques. Remarkably, sale transactions of a big distribution and sale of goods centers are used in this study.Findings– The results indicated that the proposed method has higher accuracy compared to the conventional CF method. Likewise, the clusters which have higher values were received more accurate recommendations. Another point was that the proposed method was faster on obtaining the results than the conventional method as the recommendations were performed with respect to the customers of the same cluster, while all clients were assessed in the conventional method and as a result, the calculation speed is reduced as the number of customers increases in this method.Originality/value– The results indicated that the proposed method has higher accuracy compared to the conventional CF method. Likewise, the clusters which have higher values were received more accurate recommendations. This is very important for businesses and trade centers as more than 80 percent of their profits come from valued customers and hence, recommendations with higher accuracy to these valued customers lead to more profits to sales centers. Since the valued customers were calculated in the proposed method and the value of each customer was distinguished for sales representatives, the accomplished recommendations can be coordinated with sales’ strategies to make it more targeted.