Conference Proceeding Article
As smartphones have become prevalent, mobile advertising is getting significant attention as being not only a killer application in future mobile commerce, but also as an important business model of emerging mobile applications to monetize them. In this paper, we present AdNext, a visit-pattern-aware mobile advertising system for urban commercial complexes. AdNext can provide highly relevant ads to users by predicting places that the users will next visit. AdNext predicts the next visit place by learning the sequential visit patterns of commercial complex users in a collective manner. As one of the key enabling techniques for AdNext, we develop a probabilistic prediction model that predicts users’ next visit place from their place visit history. To automatically collect the users’ place visit history by smartphones, we utilize Wi-Fi-based indoor localization. We demonstrate the feasibility of AdNext by evaluating the accuracy of the prediction model. For the evaluation, we used a dataset collected from COEX Mall, the largest commercial complex in South Korea. Also, we implemented an initial prototype of AdNext with the latest smartphones, and deployed it in COEX Mall.
Mobile advertising, Sequential visit patterns, Prediction models, Wi-Fi localization, User survey
Proceedings of the 12th Workshop on Mobile Computing Systems and Applications (HotMobile'11)
KIM, Byoungjip; HA, Jin-Young; LEE, SangJeong; KANG, Seungwoo; LEE, Youngki; RHEE, Yunseok; Nachman, Lama; and SONG, Junehwa.
AdNext: A Visit-Pattern-Aware Mobile Advertising System for Urban Commercial Complexes. (2011). Proceedings of the 12th Workshop on Mobile Computing Systems and Applications (HotMobile'11). 7-12. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2078
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