TY - GEN
T1 - Evaluation of Analysis Model for Products with Coefficients of Binary Classifiers and Consideration of Way to Improve
AU - Yamagiwa, Ayako
AU - Goto, Masayuki
N1 - Funding Information:
This work was supported by JSPS KAKENHI Grant Number 21H04600.
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Purchasing actions on e-commerce sites have become very common for general consumers in recent years. Products that were used to be bought at offline shops are purchased are also handled. Such products, like gifts or durable consumer goods, are often purchased infrequently and whose prefer items change each time they are purchased. A lot of methods are proposed for analysis purchase history data in order to improve customer satisfaction. However, most of them focus on the co-occurrence relationship between customers and products and treat products purchased by the same customer as similar. Then, it is difficult to use the conventional product analysis methods that have been proposed for purchase history data is difficult for some kinds of data mentioned before. Therefore, the authors have proposed an analysis method with extracting features of products by using the coefficients of binary classifiers that discriminates product purchases or not. In this study, we conduct experiments with artificial data in order to evaluate our method. Specifically, we verify how accurately the coefficients can be estimated and under what circumstances they can be estimated more accurately.
AB - Purchasing actions on e-commerce sites have become very common for general consumers in recent years. Products that were used to be bought at offline shops are purchased are also handled. Such products, like gifts or durable consumer goods, are often purchased infrequently and whose prefer items change each time they are purchased. A lot of methods are proposed for analysis purchase history data in order to improve customer satisfaction. However, most of them focus on the co-occurrence relationship between customers and products and treat products purchased by the same customer as similar. Then, it is difficult to use the conventional product analysis methods that have been proposed for purchase history data is difficult for some kinds of data mentioned before. Therefore, the authors have proposed an analysis method with extracting features of products by using the coefficients of binary classifiers that discriminates product purchases or not. In this study, we conduct experiments with artificial data in order to evaluate our method. Specifically, we verify how accurately the coefficients can be estimated and under what circumstances they can be estimated more accurately.
KW - Binary classifiers
KW - Feature embedding
KW - Product analysis
UR - http://www.scopus.com/inward/record.url?scp=85133189506&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85133189506&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-05064-0_29
DO - 10.1007/978-3-031-05064-0_29
M3 - Conference contribution
AN - SCOPUS:85133189506
SN - 9783031050633
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 388
EP - 402
BT - Social Computing and Social Media
A2 - Meiselwitz, Gabriele
PB - Springer Science and Business Media Deutschland GmbH
T2 - 14th International Conference on Social Computing and Social Media, SCSM 2022 Held as Part of the 24th HCI International Conference, HCII 2022
Y2 - 26 June 2022 through 1 July 2022
ER -