TY - JFULL AU - Yeonjeong Lee and Kyoung-jae Kim and Youngtae Kim PY - 2013/11/ TI - Recommender Systems Using Ensemble Techniques T2 - International Journal of Computer and Information Engineering SP - 1347 EP - 1351 VL - 7 SN - 1307-6892 UR - https://publications.waset.org/pdf/17197 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 82, 2013 N2 - This study proposes a novel recommender system that uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user’s preference. The proposed model consists of two steps. In the first step, this study uses logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. Then, this study combines the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. In the second step, this study uses the market basket analysis to extract association rules for co-purchased products. Finally, the system selects customers who have high likelihood to purchase products in each product group and recommends proper products from same or different product groups to them through above two steps. We test the usability of the proposed system by using prototype and real-world transaction and profile data. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The results also show that the proposed system may be useful in real-world online shopping store. ER -