발간년도 : [2019]
| 논문정보 |
|
| 논문명(한글) |
[Vol.14, No.5] A Performance Evaluation Analysis of Product Recommendation Techniques |
|
| 논문투고자 |
Chul-Jin Kim, Ji-Hyun Jeong, Cheon-Woo Jo, Je-Kwang Yoo |
|
| 논문내용 |
In the vast product information of the e-commerce, the user needs a lot of effort to find the required product, and the seller may affect the sales if the product is not provided quickly to the user. Accordingly, the e-commerce company provides a recommendation service based on the user's past purchase information so that the user can provide a product required by the user. The recommendation techniques for providing recommendation services include a collaborative filtering recommendation technique that derives recommendation information through a relationship between users or products and a recommendation technique that utilizes a deep learning technology based on machine learning. In this paper, we study user-based collaborative filtering recommendation and item-based collaborative filtering recommendation as collaborative filtering recommendation techniques, and RNN, LSTM, and Word2Vec recommendation techniques as deep recommendation techniques. In this paper, we evaluate the recommendation performance based on the e-commerce purchase information for these recommendation techniques. As a metric for evaluating the recommendation performance, it analyzes the recommendation performance using accuracy, recall, and F1 measure. The results of the validation of the recommendation performance showed that the LSTM recommendation technique had the best recommendation performance, and that the recommendation performance was the best when the number of recommendations was Top-10. Based on the recommended performance evaluation procedure and evaluation results proposed in this study, it can be referred to when analyzing the performance of recommendations in various fields. |
|
| 첨부논문 |
|
|
|
|
|
자료실 > 논문지 
