고객-상품 구매행렬 기반 전자상거래 추천자 시스템의 시뮬레이션 분석

Recommender systems for E-commerce site receive information from customers about which products they are interested in, and recommend products that are likely to fit their needs. This paper investigates the efficiencies of the collaborative filtering method and a SVD(singular value decomposition)-based recommender system for the purpose of producing useful recommendations to customers when large-scale customer-product purchase data are available.Reduced product-dimensionality obtained from SVD requires less computational effort for obtaining the neighborhood for target customer, thus it may improve the efficiency of recommendation performance. Simulation experiments on synthetic customer-product purchase data show SVD-based recommender system yields a better performance than the CF method with respect to the recall, precision and F1 measure. In applying SVD recommender system, the recommendation quality increases as the size of the neighborhood increases up to 30, but above that point, the improvement gains diminish a little. Our simulation results show an appropriate number of products for recommendation would be 10 in term of the errors of false positives. Around 10, the recall is not small, and both precision and F1 measure show good performances. We consider these informations may be useful in applying recommender system.