a novel mobile personalized recommended method based on money flow model for stock exchange

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ID: 186594
2014
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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.
Reference Key
xu2014mathematicala Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Qingzhen Xu;Jiayong Wu;Qiang Chen
Journal journal of power sources
Year 2014
DOI
10.1155/2014/353910
URL
http://dx.doi.org/10.1155/2014/353910
Keywords
civil engineering (general)mathematics engineering (general)

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