발간년도 : [2020]
| 논문정보 |
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| 논문명(한글) |
[Vol.15, No.4] A Dynamic Recommendation Architecture and Procedure Based on Elasticsearch |
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| 논문투고자 |
Ji-Hyun Jeong, Chul-Jin Kim |
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| 논문내용 |
E-commerce product recommendation uses various recommendation techniques. The recommendation techniques that are generally applied include the collaborative filtering technique, the user based collaborative filtering, and the item based collaborative filtering technique, and utilizes recommended techniques based on artificial intelligence technologies such as Recurrent Netural Network (RNN) and Long Short Term Memory (LSTM). The recommendation data generated through these recommendation techniques are stored in a database and are recommended in the form of static data by user actions (search, order, etc.). However, the recommendation through the static recommendation product list is limited in improving the purchasing power of the user. Providing a variety of recommended products and real-time properties, it is possible to further improve purchasing power if it is possible to dynamically construct a recommended product list. Therefore, this paper proposes an architecture and procedure for providing dynamic recommendations. The proposed dynamic recommendation architecture constructs a dynamic recommendation product by applying a static recommendation product based on Elasticsearch. In the experiment, accuracy was compared and analyzed by applying the same transaction data to existing recommendation techniques, and it was confirmed that the accuracy of the dynamic recommendation technique proposed in this paper is improved compared to the existing recommendation techniques. |
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