A Concept Lattice-Based Kernel Method for Mining Knowledge in an M-Commerce System

Authors:
Qiudan Li;Chunheng Wang;Guanggang Geng;Ruwei Dai
Affiliations:
Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing,;Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing,;Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing,;Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing,
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Year:
2007

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Abstract

With the vast amount of mobile user information available today, mining knowledge of mobile users is getting more and more important for a mobile commerce (M-commerce) system. Vector space model (VSM) is one of the most popular methods to achieve the above goal. Unfortunately, it can not identify the latent information in the user feature space, which decreases the quality of personalized services. In this paper, we present a concept-lattice based kernel method for mining the hidden user knowledge. The main idea is to employ concept lattice for constructing item proximity matrix, and then embed it into a kernel function, which transforms the original user feature space into a user concept space, and at last, perform personalized services in the user concept space. The experimental results demonstrate that our method is more encouraging than VSM.