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A Novel APP Recommendation Method Based on SVD and Social Influence

  • Qiudang Wang
  • Xiao Liu
  • Shasha Zhang
  • Yuanchun Jiang
  • Fei Du
  • Yading Yue
  • Yu Liang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9529)

Abstract

The market for Mobile Applications (APP for short) is perhaps the most thriving sector nowadays in the software industry with about 4 million APPs around the world. APP recommendation is playing an increasingly important role in every APP store to enhance user experience and raise revenue. Existing recommendation strategies are mainly based on user’s individual information while their social relations are often neglected. However, it is an intuitive knowledge that users tend to be affected by their friends’ recommendation in the choice of APPs. Therefore, it is worth investigating whether and how social influence can be employed for APP recommendation. In this paper, to answer the above question, we propose a novel APP recommendation method based on SVD (Singular Value Decomposition) algorithm and social influence which is defined by an extended CD (Credit Distribution) model. The experimental results based on the real-world datasets from Tencent APP Store demonstrate that our proposed method with social influence can achieve a better recommendation results than conventional SVD based algorithm without social relations.

Keywords

Recommendation Social network Mobile applications SVD 

Notes

Acknowledgements

The research work reported in this paper is partly supported by CCF-Tencent Open Fund CCF-TencentRAGR20140109, National Natural Science Foundation of China (NSFC) under No. 61300042, No. 71490725, No. 71302064, No. 71371062, National Key Basic Research Program of China (2013CB329600), Research Fund for the Doctoral Program of Higher Education of China (Project 20120111120029), and Shanghai Knowledge Service Platform Project No. ZF1213.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Qiudang Wang
    • 1
  • Xiao Liu
    • 1
    • 2
  • Shasha Zhang
    • 1
  • Yuanchun Jiang
    • 3
  • Fei Du
    • 3
  • Yading Yue
    • 4
  • Yu Liang
    • 4
  1. 1.Shanghai Key Laboratory of Trustworthy ComputingEast China Normal UniversityShanghaiChina
  2. 2.School of Information TechnologyDeakin UniversityMelbourneAustralia
  3. 3.School of ManagementHefei University of TechnologyHefeiChina
  4. 4.Knowledge Discovery Team, Department of Social Networks OperationTencent Inc.ShenzhenChina

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