Social Recommendation Algorithm Research based on Trust Influence

Social Recommendation Algorithm Research based on Trust Influence

Xue Yi

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Hong Yinghan

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Chen Pinghua

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Guangdong University of Technology

Social Recommendation Algorithm Research based on Trust Influence

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Abstract

Cold start and data sparsity greatly affect the recommendation quality of collaborative filtering. To solve these problems, social recommendation algorithms introduce the corresponding user trust information in social network, however, these algorithms typically utilize only adjacent trusted user information while ignoring the social network connectivity and the differences in the trust influence between indirect users, which leads to poor accuracy. For this deficiency, this paper proposes a social recommendation algorithm based on user influence strength. First of all, we get the user influence strength vector by iterative calculation on social network and then achieve a relatively complete user latent factor according to near-impact trusted user behavior. Depending on such a user influence vector, we integrate user-item rating matrix and the trust influence information. Experimental results show that it has a better prediction accuracy, compared to the state-of-art society recommendation algorithms.

References

10 Cites in Article

Reference Format

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Xue Yi. 2016. \u201cSocial Recommendation Algorithm Research based on Trust Influence\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 16 (GJCST Volume 16 Issue C1).

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GJCST Volume 16 Issue C1

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

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Classification

GJCST-C Classification K.4.2 B.2.4

Submission ReceivedDecember 12, 2015
Peer Review Double Blind
Handling Editor
Accepted January 4, 2016
Published January 15, 2016

Version of record

v1.2

Issue date

April 22, 2016

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Article Matrices

Total Score: 133

Country: China

Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering

Authors: Xue Yi, Hong Yinghan, Chen Pinghua (PhD/Dr. count: 0)

View Count (all-time): 327

Total Views (Real + Logic): 7878

Total Downloads (simulated): 1995

Publish Date: 2016 04, Fri

Monthly Totals (Real + Logic):

Month 1: 54 views
Month 2: 30 views
Month 3: 69 views
Month 4: 46 views
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Total Views: 7878

Total Downloads: 1995

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