An Open Architecture and Open API for e-Commerce Recommendation Model Development Platform

Kim Chul Jin; Jeong， JiHyun; Cheon-Woo Jo; ByunDongHun

doi:10.34163/jkits.2020.15.6.008

e-Commerce recommendation service is an essential function and plays an important role in increasing sales since it is provided in connection with functions such as product search, order processing, and shopping cart. This recommendation service requires a high level of technology from developers developing e-commerce, so it is developed by a specific artificial intelligence engineer or applied by introducing an external recommendation solution. Integration of recommended services by external solutions or external development companies cannot satisfy the requirements of e-commerce services to be developed, and cannot provide rapid maintenance due to frequent data changes. Accordingly, research on a generalized development platform for generalizing and providing recommendation services suitable for a specific domain or developing a recommendation service is being actively conducted. Amazon Personalize service and Microsoft Azure Machine Learning service are generalized tools for developing recommended services by developers. However, these recommendation model development tools have a workload of defining essential data information for training data required to generate a recommendation model. In this paper, we derive a learning algorithm without defining data by using an association analysis algorithm between data for analyzing learning data. Also, based on the derived learning algorithm, we propose an Open API for developing and verifying a recommendation model. In the experiment, the learning algorithm is derived and the open API is verified by using the open transaction data of the e-commerce transaction. Through this, the suitability of the open architecture of the recommendation model development platform is verified.

Citation status

KCI Citation Counts (1)
열림/닫힘 버튼
REFERENCES (16)
열림/닫힘 버튼
* 2022년 이후 발행 논문의 참고문헌은 현재 구축 중입니다.

[confproc] B. M. Sarwar / 2000 / Analysis of recommendation algorithms for e-commerce / Proc. of 2nd ACM Conference on Electronic Commerce : 158~167

[web] / 2020 / Amazon Personalize / https://aws.amazon.com/ko/personalize/?nc2=h_ql_prod_ml_per

[confproc] D. Stern / Matchbox: large scale online bayesian recommendations / Proceedings of the 18th international conference on World wide web

[web] / 2020 / AWS Dataset Schema / https://docs.aws.amazon.com/personalize/latest/dg/how-it-works-dataset-schema.html

[web] / 2020 / Microsoft Azure Machine Learning / https://studio.azureml.net/

[journal] 소경영 / 2015 / Design of Bi-directional Recommend Calligraphy Contents Open-market Platform / 멀티미디어학회논문지 / 한국멀티미디어학회 18(12) : 1586~1593 / http://dx.doi.org/10.9717/kmms.2015.18.12.1586

[confproc] W. J. Yang / 2020 / Personal consumption pattern forecast and financial products recommendation with machine learning / Proceedings of The Korean Institute of Information Scientists and Engineers : 1402~1403

[web] / 2020 / ISO 8601-1:2019, Date and time-Representations for information interchange / https://www.iso.org/standard/70907.html

[web] / 2020 / Recurrent neural network(RNN) tutorial-part 1 / http://aikorea.org/blog/rnn-tutorial-1

[journal] Y. Bengio / 1994 / Learning long-term dependencies with gradient descent is difficult / Journal of IEEE Transactions on Neural Networks 5(2) : 157~166

[journal] S. Hochreiter / 1997 / Long short-term memory / Neural Computation 9(8) : 1735~1780

[web] / 2020 / Movie lens dataset / https://grouplens.org/datasets/movielens/

[web] / 2020 / UCI online retail dataset / https://archive.ics.uci.edu/ml/datasets/online+retail

[web] / 2020 / Retail rocket dataset / https://www.kaggle.com/retailrocket/ecommerce-dataset

[journal] J. Herlocker / 2002 / An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms / Information Retrieval 5(4) : 287~310

[web] / 2020 / Accuracy and precision / https://en.wikipedia.org/wiki/Accuracy_and_precision

이 논문은 한국연구재단의 지원을 받아 작성되었습니다.

KJC Korea
Journal Central

Statistics

Tools

Sharing

Issue List

Citation status