Recommendation-Aware Smartphone Sensing System

 

Mu-Yen Chen¹, Ming-Ni Wu¹, Chia-Chen Chen*², Young-Long Chen³ and Hsien-En Lin¹

 

¹ Department of Information Management, National Taichung University of Science and Technology, Taichung, Taiwan.

² Department of Management Information Systems, National Chung Hsing University, Taichung, Taiwan. *emily@nchu.edu.tw

³ Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, Taichung, Taiwan.

 

Abstract

The context-aware concept is to reduce the gap between users and information systems so that the information systems actively get to understand users' context and demand and in return provide users with better experience. This study integrates the concept of context-aware with association algorithms to establish the context-aware recommendation systems (CARS). The CARS contains three modules and provides the product recommendations for users with their smartphone. First, the simple RSSI Indoor localization module (SRILM) locates the user position and detects the context information surrounding around users. Second, the Apriori recommendation module (ARM) provides effective recommended product information for users through association rules mining. The appropriate product information can be received effectiveness and greatly enhanced the recommendation service.

Keywords: Context-aware, smartphone sensing, recommendation service.

 

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Aknowledgments

The authors thank the support of National Scientific Council (NSC) of the Republic of China (ROC) to this work under Grant No. NSC-101-2622-E-025-002-CC3, NSC-102-2622-E-005-014-CC3, and NSC-102-2410-H-005-064. The authors also gratefully acknowledge the Editor and anonymous reviewers for their valuable comments and constructive suggestions.

 

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