Earticle

초록

영어

Most studies on recommender systems to evaluate recommendation performances focus on offline evaluation methods utilizing past customer transaction records. However, evaluating recommendation performance through real-world stimulation becomes challenging. Moreover, such methods cannot evaluate the duration of the recommendation effect. This study measures the personalized recommendation (stimulus) effect when the product recommendation to customers leads to actual purchases and evaluates the duration of the stimulus personalized recommendation effect leading to purchases. The results revealed a 4.58% improvement in recommendation performance in the online environment compared with that in the offline environment. Furthermore, there is little difference in recommendation performance in offline experiments by period, whereas the recommendation performance declines with time in online experiments.

목차

ABSTRACT
Ⅰ. Introduction
Ⅱ. Related Work
Ⅲ. Methodology
3.1. Customer Profile Generation
3.2. Offline and Online Dataset Split
3.3. Recommendation Algorithm
3.4. Comparison of Effects Based on the Recommendation Duration
Ⅳ. Experiments
4.1. Dataset Collection
4.2. Evaluation Metrics
4.3. Experiment Results
Ⅴ. Discussion and Conclusion

저자

• Qinglong Li [ Ph.D. Candidate, Department of Big Data Analytics, Kyung Hee University, Korea ]
• Jaeho Jeong [ Ph.D., Department of Business Administration, Kyung Hee University, Korea ]
• Dongeon Kim [ M.S., Department of Big Data Analytics, Kyung Hee University, Korea ]
• Xinzhe Li [ Ph.D. Student, Department of Big Data Analytics, Kyung Hee University, Korea ]
• Ilyoung Choi [ Assistant Professor, Division of Business Administration, Seo Kyeong University, Korea ]
• Jaekyeong Kim [ Professor, Department of Big Data Analytics and School of Management, Kyung Hee University, Korea ] Corresponding Author

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