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)


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.


Recommendation Social network Mobile applications SVD 



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.


  1. 1.
    Research and Markets, The World’s Leading E-commerce Companies 2014. Accessed 1 July 2015
  2. 2.
    Statista, Number of Apps Available in Leading App Stores as of July 2015. Accessed 1 July 2015
  3. 3.
    Tech in Asia, 10 Alternative Android App Stores in China. Accessed 1 July 2015
  4. 4.
    Zhou, Y., Wilkinson, D., Schreiber, R., Pan, R.: Large-scale parallel collaborative filtering for the netflix prize. In: Fleischer, R., Xu, J. (eds.) AAIM 2008. LNCS, vol. 5034, pp. 337–348. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Business Insider, Clickthrough Rate and Cost-per-click on Facebook for Selected Sectors. Accessed 1 July 2015
  6. 6.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295 (2001)Google Scholar
  7. 7.
    Ma, H., Yang, H., Lyu, M.R., King, I.: SoRec: social recommendation using probabilistic matrix factorization. In: Proceedings of the 2008 ACM International Conference on Information and Knowledge Management, pp. 931–940 (2008)Google Scholar
  8. 8.
    Amit, G., Bonchi, F., Lakshmanan, L.V.S.: A data-based approach to social influence maximization. Proc. VLDB Endowment 5(1), 73–84 (2011)CrossRefGoogle Scholar
  9. 9.
    Chen, N., Hoi, S.C.H., Li, S., Xiao, X.: SimApp: a framework for detecting similar mobile applications by online kernel learning. In: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, pp. 305–314 (2015)Google Scholar
  10. 10.
    Yin, P., Luo, P., Lee, W., Wang, M.: App recommendation: a contest between satisfaction and temptation. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 395–404 (2013)Google Scholar
  11. 11.
    Davidsson, C., Moritz, S.: Utilizing implicit feedback and context to recommend mobile applications from first use. In: Proceedings of the 2011 Workshop on Context-Awareness in Retrieval and Recommendation, pp. 19–22 (2011)Google Scholar
  12. 12.
    Girardello, A., Michahelles, F.: AppAware: which mobile applications are hot? In: Proceedings of the 12th International Conference on Human Computer Interaction with Mobile Devices and Services, pp. 431–434 (2010)Google Scholar
  13. 13.
    Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 230–237 (1999)Google Scholar
  14. 14.
    Yan, B., Chen, G.: AppJoy: personalized mobile application discovery. In: Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services, pp. 113–126 (2011)Google Scholar
  15. 15.
    Ma, C.: A guide to singular value decomposition for collaborative filtering. Technical report. Accessed 1 July 2015
  16. 16.
    Zhang, M., Dai, C., Ding, C., Chen, E.: Probabilistic solutions of influence propagation on social networks. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, pp. 429–438 (2013)Google Scholar
  17. 17.
    Tang, J., Sun, J., Wang, C., Yang, Z.: Social influence analysis in large-scale networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 807–816 (2009)Google Scholar
  18. 18.
    Ma, H., Zhou, T.C., Lyu, M.R., King, I.: Improving recommender systems by incorporating social contextual information. ACM Trans. Inf. Syst., pp. 219–230 (2011)Google Scholar
  19. 19.
    Goyal, A., Bonchi, F., Lakshmanan, L.V.S.: Learning influence probabilities in social networks. In: Proceedings of the 3rd ACM International Conference on Web Search and Data Mining, pp. 241–250 (2010)Google Scholar
  20. 20.
    Cha, M., Haddadi, H., Benevenuto, F., Gummadi, K.P.: Measuring user influence in twitter: the million follower fallacy. In: Proceedings of International AAAI Conference on Weblogs & Social (2010)Google Scholar
  21. 21.
    Anagnostopoulos, A., Kumar, R., Mahdian, M.: Influence and correlation in social networks. In: Proceeding of ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 7–15 (2008)Google Scholar
  22. 22.
    Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 137–146 (2003)Google Scholar

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

Personalised recommendations