Journal of Information Processing
Online ISSN : 1882-6652
ISSN-L : 1882-6652
Content-based Stock Recommendation Using Smartphone Data
Kohsuke KubotaHiroyuki SatoWataru YamadaKeiichi OchiaiHiroshi Kawakami
Author information

2022 Volume 30 Pages 361-371


The number of investors holding risky assets in Japan is much lower than that in western countries even though it is an effective way for building investor assets. Although Japanese investment companies offer a service to invest in points through coalition loyalty programs instead of actual currency to address this situation, the problem still persists. One reason for this is that novice investors do not know in which stocks to invest. One possible solution is recommending stocks; however, we still face the cold-start problem because there is no transaction history for novice investors. In this study, we propose a novel content-based recommendation approach that utilizes touchpoint information, e.g., payment and app usage data, on smartphones in daily life. This approach employs user-weighted recency, frequency, and monetary, called UW-RFM and a complementary module to comply with Japanese guidelines that prohibit presenting only a small number of companies and establishing a minimum number of companies to be presented. We conduct an online evaluation to validate the effectiveness of the proposed approach in an actual investment service. The evaluation results show that the proposed approach motivates users to invest more, i.e., 0.352 more clicks on the recommendation area and 3, 016 points (yen), than the baseline method that does not consider touchpoint information.

Content from these authors
© 2022 by the Information Processing Society of Japan
Previous article Next article