With the development of mobile internet and smart devices, location-based services (LBS) have developed rapidly and attracted more and more users. The rapid growth in the number of services has left users at a loss and trapped in the plight of information overload. The availability of a large amount of user interaction data makes it possible to provide more personalized and accurate recommendation services. However, in mobile scenarios, multiple influencing factors such as the diversity of user preferences, the variability of user behavior, and the dynamics of spatiotemporal contexts bring great challenges to recommendation services. To accurately capture the preferences of mobile users in dynamic contexts, we propose an Inherent and Contextual Preference-aware Attention Network (ICPAN) for online recommendation in location-based services.
Authors
Haiting Zhong received her B.S. degree in Software Engineering from Shandong University in 2020. She is currently working on her M.S. degree at School of Software of Shandong University.
Wei He, Associate Professor and Master Supervisor of Shandong University. Member of Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University & Nanyang Technological University.
Lizhen Cui, Dean of School of Software, Shandong University, Co Dean of Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University & Nanyang Technological University.
Lei Liu, Distinguished Professor of Qilu Young Scholars of Shandong University, Doctoral Supervisor, Assistant Dean of Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University & Nanyang Technological University.
