Earticle

초록

영어

The purpose of this study is to incorporate telemarketing processes to improve telemarketing performance. For this application, we have attempted to mix the model of machine learning to extract potential customers with personalisation techniques to derive recommended products from actual contact. Most of traditional recommendation systems were mainly in ways such as collaborative filtering, which predicts items with a high likelihood of future purchase, based on existing purchase transactions or preferences for products. But, under these systems, new users or items added to the system do not have sufficient information, and generally cause problems such as a cold start that can not obtain satisfactory recommendation items. Also, indiscriminate telemarketing attempts can backfire as they increase the dissatisfaction and fatigue of customers who do not want to be contacted. To this purpose, this study presented a multi-purpose hybrid recommendation algorithm to achieve two goals: to select customers with high possibility of contact, and to recommend products to selected customers. In addition, we used subscription data from telemarketing agency that handles insurance products to derive realistic applicability of the proposed recommendation system. Our proposed recommendation system would certainly solve the cold start and scarcity problem of existing recommendation algorithm by using contents information such as customer master information and telemarketing history. Also. the model could show excellent performance not only in terms of overall performance but also in terms of the recommendation success rate of the unpopular product.

목차

ABSTRACT
Ⅰ. Introduction
Ⅱ. Literature Review
2.1. Collaborative Filtering-based Approach
2.2. Rule-based Approach
2.3. Hybrid-based Approach
Ⅲ. Proposed Method
3.1. Data
3.2. Development of Multi-Purpose Hybrid Recommendation Model
3.3. Model Performance Evaluation and Final Algorithm Selection
Ⅳ. Evaluation
4.1. Model Performance Comparison of All Products
4.2. Model Performance Comparison of Unpopular Products
Ⅴ. Conclusions and Future Work
5.1. Conclusions
5.2. Limitations and Future Work
Acknowledgement

키워드

저자

• Hyung Su Kim [ Associate Professor, Department of Industry & Management Engineering, Hansung University, Korea ]
• Sangwon Lee [ Associate Professor, Department of Computer & Software Engineering, Wonkwang University, Korea ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  Asia Pacific Journal of Information Systems

• 간기

  계간

• pISSN

  2288-5404

• eISSN

  2288-6818

• 수록기간

  1990~2026

• 등재여부

  KCI 등재,SCOPUS

• 십진분류

  KDC 325 DDC 658