A Dynamic Recommendation Architecture and Procedure Based on Elasticsearch (Elasticsearch 기반의 동적 추천 아키텍쳐 및 절차)

Jeong， JiHyun (정지현); Kim Chul Jin (김철진)

doi:10.34163/jkits.2020.15.4.002

A Dynamic Recommendation Architecture and Procedure Based on Elasticsearch

Journal of Knowledge Information Technology and Systems
Abbr : JKITS
2020, 15(4), pp.463-472
DOI : 10.34163/jkits.2020.15.4.002
Publisher : Korea Knowledge Information Technology Society
Research Area : Interdisciplinary Studies > Interdisciplinary Research
Received : June 15, 2020
Accepted : August 10, 2020
Published : August 31, 2020

Jeong， JiHyun ¹, Kim Chul Jin ¹

¹인하공업전문대학

Accredited

ABSTRACT

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.

KEYWORDS

Dynamic recommendation, Static recommendation, Elasticsearch, Recommendation architecture, Accuracy

Citation status

* References for papers published after 2023 are currently being built.

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This paper was written with support from the National Research Foundation of Korea.

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Journal of Knowledge Information Technology and Systems 2023 KCI Impact Factor : 0.89

A Dynamic Recommendation Architecture and Procedure Based on Elasticsearch

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Citation status

* References for papers published after 2023 are currently being built.

Journal of Knowledge Information Technology and Systems 2023 KCI Impact Factor : 0.89

A Dynamic Recommendation Architecture and Procedure Based on Elasticsearch

ABSTRACT

KEYWORDS

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* References for papers published after 2023 are currently being built.