Yeonjeong Lee and Kyoung-jae Kim and Youngtae Kim
Recommender Systems Using Ensemble Techniques
1348 - 1351
2013
7
10
International Journal of Computer and Information Engineering
https://publications.waset.org/pdf/17197
https://publications.waset.org/vol/82
World Academy of Science, Engineering and Technology
This study proposes a novel recommender system that uses data mining and multimodel 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 multimodel ensemble techniques such as bagging and bumping. In the second step, this study uses the market basket analysis to extract association rules for copurchased 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 realworld 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 realworld online shopping store.
Open Science Index 82, 2013