TY - GEN
T1 - Purchasing Behavior Analysis Model that Considers the Relationship Between Topic Hierarchy and Item Categories
AU - Sakai, Yuta
AU - Matsuoka, Yui
AU - Goto, Masayuki
N1 - Funding Information:
In this paper, we used “Rakuten Dataset” (https://rit.rakuten. com/data release/) provided by Rakuten Group, Inc. via IDR Dataset Service of National Institute of Informatics, Japan. We gratefully acknowledge the provision of the precious dataset. This work was supported by JSPS for Scientific Research No. 21H04600.
Funding Information:
Acknowledgements. In this paper, we used “Rakuten Dataset” (https://rit.rakuten. com/data release/) provided by Rakuten Group, Inc. via IDR Dataset Service of National Institute of Informatics, Japan. We gratefully acknowledge the provision of the precious dataset. This work was supported by JSPS for Scientific Research No. 21H04600.
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - With the spread of EC sites, it has become an important work for companies to analyze user preferences contained in accumulated purchase history data and utilize them in marketing measures. A topic model is well known as a method for analyzing user preferences from purchase history data, and a model assuming hierarchy of topics has been proposed as an extension method. The previously proposed PAM (Pachinko Allocation Model) is a highly expressive model in which all upper and lower topics are connected by a network and the relationships between multiple topics can be analyzed. However, PAM is easily affected by the initial values of learning parameters, and it is difficult to obtain stable topics, so the interpretation of the estimated topics becomes unstable. It is dangerous to make business decisions based on the interpretation of such unstable results. Therefore, in this research, instead of using the hierarchy of topics estimated based on the user’s purchasing behavior, we use information with a hierarchical structure of “product categories” given by the EC site for managing items. Therefore, we propose a method that is useful for studying measures and that enables hierarchical topic analysis. Finally, the proposed method is applied to the evaluation history data of the actual EC site to analyze the user’s preference and show its usefulness.
AB - With the spread of EC sites, it has become an important work for companies to analyze user preferences contained in accumulated purchase history data and utilize them in marketing measures. A topic model is well known as a method for analyzing user preferences from purchase history data, and a model assuming hierarchy of topics has been proposed as an extension method. The previously proposed PAM (Pachinko Allocation Model) is a highly expressive model in which all upper and lower topics are connected by a network and the relationships between multiple topics can be analyzed. However, PAM is easily affected by the initial values of learning parameters, and it is difficult to obtain stable topics, so the interpretation of the estimated topics becomes unstable. It is dangerous to make business decisions based on the interpretation of such unstable results. Therefore, in this research, instead of using the hierarchy of topics estimated based on the user’s purchasing behavior, we use information with a hierarchical structure of “product categories” given by the EC site for managing items. Therefore, we propose a method that is useful for studying measures and that enables hierarchical topic analysis. Finally, the proposed method is applied to the evaluation history data of the actual EC site to analyze the user’s preference and show its usefulness.
KW - EC site
KW - Purchase history data
KW - Topic model
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U2 - 10.1007/978-3-031-05064-0_26
DO - 10.1007/978-3-031-05064-0_26
M3 - Conference contribution
AN - SCOPUS:85133195918
SN - 9783031050633
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 344
EP - 358
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 -