Research on the fusion mechanism of cooperative embedded filtering and crowd content recommendation.
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| Title: | Research on the fusion mechanism of cooperative embedded filtering and crowd content recommendation. |
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| Authors: | Yu-yun, Chen1 yuyuncchen@sina.com |
| Source: | EURASIP Journal on Embedded Systems. 8/30/2016, Vol. 2017 Issue 1, p1-7. 7p. |
| Subjects: | Embedded computer systems, Cooperative processing, Information filtering systems, Internet servers, Algorithms |
| Abstract: | Internet simultaneous services of large-scale users will lead to server overload and information failure. Static content recommendation system cannot adapt to the dynamic similarity characteristics of users. So, how to perceive the high accuracy of recommendation scheme in dynamic environment becomes one of the key techniques in application of educational information and embedded application. We analyze the problem of low efficiency and high error of the recommendation technology based on the user's requirement. And, we proposed the cooperative filtering recommendation system based on the dynamic similarity of different users. In order to improve the prediction accuracy of cooperative filtering algorithm, the user's target content would be processed with crowd scheme. Then, the system is fused with the recommendation system. According to the weights of the fusion, the crowd recommended fusion scheme are proposed. The experimental results show that the fusion mechanism of cooperative embedded filtering and crowd content recommendation has obvious advantages in terms of content recommendation accuracy, reliability, and convergence speed. [ABSTRACT FROM AUTHOR] |
| Copyright of EURASIP Journal on Embedded Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 117761153 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Research on the fusion mechanism of cooperative embedded filtering and crowd content recommendation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yu-yun%2C+Chen%22">Yu-yun, Chen</searchLink><relatesTo>1</relatesTo><i> yuyuncchen@sina.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22EURASIP+Journal+on+Embedded+Systems%22">EURASIP Journal on Embedded Systems</searchLink>. 8/30/2016, Vol. 2017 Issue 1, p1-7. 7p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Embedded+computer+systems%22">Embedded computer systems</searchLink><br /><searchLink fieldCode="DE" term="%22Cooperative+processing%22">Cooperative processing</searchLink><br /><searchLink fieldCode="DE" term="%22Information+filtering+systems%22">Information filtering systems</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+servers%22">Internet servers</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Internet simultaneous services of large-scale users will lead to server overload and information failure. Static content recommendation system cannot adapt to the dynamic similarity characteristics of users. So, how to perceive the high accuracy of recommendation scheme in dynamic environment becomes one of the key techniques in application of educational information and embedded application. We analyze the problem of low efficiency and high error of the recommendation technology based on the user's requirement. And, we proposed the cooperative filtering recommendation system based on the dynamic similarity of different users. In order to improve the prediction accuracy of cooperative filtering algorithm, the user's target content would be processed with crowd scheme. Then, the system is fused with the recommendation system. According to the weights of the fusion, the crowd recommended fusion scheme are proposed. The experimental results show that the fusion mechanism of cooperative embedded filtering and crowd content recommendation has obvious advantages in terms of content recommendation accuracy, reliability, and convergence speed. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of EURASIP Journal on Embedded Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=117761153 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1186/s13639-016-0050-x Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 7 StartPage: 1 Subjects: – SubjectFull: Embedded computer systems Type: general – SubjectFull: Cooperative processing Type: general – SubjectFull: Information filtering systems Type: general – SubjectFull: Internet servers Type: general – SubjectFull: Algorithms Type: general Titles: – TitleFull: Research on the fusion mechanism of cooperative embedded filtering and crowd content recommendation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yu-yun, Chen IsPartOfRelationships: – BibEntity: Dates: – D: 30 M: 08 Text: 8/30/2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 16873955 Numbering: – Type: volume Value: 2017 – Type: issue Value: 1 Titles: – TitleFull: EURASIP Journal on Embedded Systems Type: main |
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