TY - GEN
T1 - Search Wandering Score
T2 - 8th IEEE International Conference on Big Data, Big Data 2020
AU - Tsubouchi, Kota
AU - Sasaki, Wataru
AU - Okoshi, Tadashi
AU - Nakazawa, Jin
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/10
Y1 - 2020/12/10
N2 - Many researchers and companies have engaged in estimating users' interests so that an online shopping system can tell what he/she wants now. This paper tackles the next challenge in online shopping, i.e., predicting the times that users go shopping online. To predict the timing of online shopping, we focus on wandering behavior in web search activities and propose a search wandering score (SWS). Online shopping behavior can be categorized into three states: wandering shop-ping, focused shopping, and others. Wandering shopping is a state in which users make purchases in high SWS situations; focused shopping is a state in which users buy things in low SWS situations. Unlike previous studies, our work is based on an analysis of large-scale data containing shopping and search logs produced by approximately 200,000 users of a real web portal site for over a year. The results of an extensive evaluation show that our methodology can predict user's future shopping behavior types with 86% accuracy. This research is the first step towards understanding the relationship between users' mental states and their online shopping behavior.
AB - Many researchers and companies have engaged in estimating users' interests so that an online shopping system can tell what he/she wants now. This paper tackles the next challenge in online shopping, i.e., predicting the times that users go shopping online. To predict the timing of online shopping, we focus on wandering behavior in web search activities and propose a search wandering score (SWS). Online shopping behavior can be categorized into three states: wandering shop-ping, focused shopping, and others. Wandering shopping is a state in which users make purchases in high SWS situations; focused shopping is a state in which users buy things in low SWS situations. Unlike previous studies, our work is based on an analysis of large-scale data containing shopping and search logs produced by approximately 200,000 users of a real web portal site for over a year. The results of an extensive evaluation show that our methodology can predict user's future shopping behavior types with 86% accuracy. This research is the first step towards understanding the relationship between users' mental states and their online shopping behavior.
KW - Online Shopping Timing
KW - Search Wandering
KW - User Classification
UR - http://www.scopus.com/inward/record.url?scp=85103861714&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85103861714&partnerID=8YFLogxK
U2 - 10.1109/BigData50022.2020.9378099
DO - 10.1109/BigData50022.2020.9378099
M3 - Conference contribution
AN - SCOPUS:85103861714
T3 - Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
SP - 1681
EP - 1688
BT - Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
A2 - Wu, Xintao
A2 - Jermaine, Chris
A2 - Xiong, Li
A2 - Hu, Xiaohua Tony
A2 - Kotevska, Olivera
A2 - Lu, Siyuan
A2 - Xu, Weijia
A2 - Aluru, Srinivas
A2 - Zhai, Chengxiang
A2 - Al-Masri, Eyhab
A2 - Chen, Zhiyuan
A2 - Saltz, Jeff
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 10 December 2020 through 13 December 2020
ER -