Estimating customer preference through store check-in histories and its use in visitor promotion

Autor:	Ken Ohta, Chiaki Doi, Masaji Katagiri, Hiroshi Inamura, Akira Ishii, Ikeda Daizo, Konishi Teppei, Takashi Araki, Hiroshi Shigeno
Rok vydání:	2017
Předmět:	Support vector machine Estimation Advertising campaign Promotion (rank) Check-in Computer science Visitor pattern Customer preference media_common.quotation_subject Advertising Preference media_common
Zdroj:	ICMU
DOI:	10.23919/icmu.2017.8330107
Popis:	This paper proposes a method to estimate the preference of customers based on store check-in histories. The proposed method can distinguish the preferences of customers who have no purchase histories. We adopt a machine learning algorithm for model acquisition. The estimation results can improve the efficiency of visitor promotion campaigns and advertising campaigns. An actual visitor promotion trial indicates the effectiveness of the proposed method.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::0f31e17b94ebc47d936a9c05415b1fd3 https://doi.org/10.23919/icmu.2017.8330107 Zobrazit plný text záznamu