A novel mobile personalized recommended method based on money flow model for stock exchange
type: Item
instance of: scholarly article
title: A novel mobile personalized recommended method based on money flow model for stock exchange
zbMATH Open document ID: 1407.90237
DOI: 10.1155/2014/353910
author: Xianqiang Yang
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 90B90
Mathematics Subject Classification ID: 90B22
Mathematics Subject Classification ID: 62H86
Mathematics Subject Classification ID: 91G80
zbMATH DE Number: 7016227
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/353910
OpenAlex ID: W2034142784
Wikidata QID: Q59064484
cites work: Second order optimality conditions for the extremal problem under inclusion constraints
description: scientific article; zbMATH DE number 7016227