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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 353910, 9 pages
http://dx.doi.org/10.1155/2014/353910
Research Article

A Novel Mobile Personalized Recommended Method Based on Money Flow Model for Stock Exchange

1School of Computer Science, South China Normal University, Guangzhou 510631, China
2Department of Computer Science, Guangdong University of Education, Guangdong 510303, China

Received 18 July 2014; Revised 2 September 2014; Accepted 9 September 2014; Published 16 October 2014

Academic Editor: Wanneng Shu

Copyright © 2014 Qingzhen Xu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Personalized recommended method is widely used to recommend commodities for target customers in e-commerce sector. The core idea of merchandise personalized recommendation can be applied to financial field, which can also achieve stock personalized recommendation. This paper proposes a new recommended method using collaborative filtering based on user fuzzy clustering and predicts the trend of those stocks based on money flow. We use M/G/1 queue system with multiple vacations and server close-down time to measure practical money flow. Based on the indicated results of money flow, we can select the more valued stock to recommend to investors. The experimental results show that the proposed method provides investors with reliable practical investment guidance and receiving more returns.