期刊
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
卷 279, 期 2, 页码 620-634出版社
ELSEVIER
DOI: 10.1016/j.ejor.2019.05.037
关键词
OR in marketing; CRM; Predictive modeling; Multi-label classifiers; Recommender systems
The objective of this paper is to evaluate multi-label classification techniques and recommender systems for cross-sell purposes in the financial services sector. We carried out three analyses using data obtained from an international financial services provider. First, we tested four multi-label classification techniques, of which the two problem transformation methods were combined with several base classifiers. Second, we benchmarked the performance of five state-of-the-art recommender approaches. Third, we compared the best performing multi-label classification and recommender approaches with each other. The results identify user-based collaborative filtering as the top performing recommender system, with a cross validated F-1 measure of 42.20% and G-mean of 42.64%. Classifier chains binary relevance with adaboost and binary relevance with random forest are the top performing multi-label classification algorithms for respectively F-1 measure and G-mean, yielding a cross-validated F-1 measure of 53.33% and G-mean of 54.37%. The statistical comparison between the best performing approaches confirms the superiority of multi-label classification techniques. Our study provides important recommendations for financial services providers, who are interested in the most effective methods to determine cross-sell opportunities. In previous studies, multi-label classification techniques and recommender systems were always investigated independently of each other. To the best of our knowledge, our study is therefore the first to compare both techniques in the financial services sector. (C) 2019 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据