Simulation Study on E-commerce Recommender System Based on a Customer-Product Purchase-Matrix

This paper investigates the efficiencies of CF method and SVD-based recommender system for producing useful recommendations to customers when large-scale customer-product purchase data are available. Simulation experiments on synthetic transaction data show SVD-based recommender system yields a better performance than the CF method. Reduced product dimensionality from SVD may be more effective in generating a reliable neighborhood than CF method, and thereby it may improve the efficiency of recommendation performance. In applying SVD-based recommender system, the recommendation quality increases as the size of the neighborhood increase up to a certain point, but after that point, the improvement gains diminish. Our simulation results also show that an appropriate number of products for recommendation would be 10 in term of the error of false positives since around this point, the recall is not small, and both precision and F1 metric appear to be maximal. Even though the recommendation quality depends upon the dimension and structure of transaction data set, we consider such information may be useful in applying recommender system