Content-based Stock Recommendation Using Smartphone Data

• Item type: Journal(1)
• 公開日: 2022-05-15
• タイトル: Content-based Stock Recommendation Using Smartphone Data
• タイトル / 言語: en
• タイトル: Content-based Stock Recommendation Using Smartphone Data
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [特集:情報システム論文] stock recommendation, propensity score matching, smartphone data
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: NTT DOCOMO, INC.
• 著者所属: NTT DOCOMO, INC.
• 著者所属: NTT DOCOMO, INC.
• 著者所属: NTT DOCOMO, INC.
• 著者所属: NTT DOCOMO, INC.
• 著者所属(英): en
• 著者所属(英): NTT DOCOMO, INC.
• 著者所属(英): en
• 著者所属(英): NTT DOCOMO, INC.
• 著者所属(英): en
• 著者所属(英): NTT DOCOMO, INC.
• 著者所属(英): en
• 著者所属(英): NTT DOCOMO, INC.
• 著者所属(英): en
• 著者所属(英): NTT DOCOMO, INC.
• 著者名: Kohsuke, Kubota; Hiroyuki, Sato; Wataru, Yamada; Keiichi, Ochiai; Hiroshi, Kawakami
• 著者名(英): Kohsuke, Kubota; Hiroyuki, Sato; Wataru, Yamada; Keiichi, Ochiai; Hiroshi, Kawakami
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: 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.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.30(2022) (online); DOI http://dx.doi.org/10.2197/ipsjjip.30.361; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: 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.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.30(2022) (online); DOI http://dx.doi.org/10.2197/ipsjjip.30.361; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 63, 号 5, 発行日 2022-05-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764