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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Research and Markets, The World’s Leading E-commerce Companies 2014. http://www.researchandmarke-ts.com/research/dtxzvc/the_worlds. Accessed 1 July 2015
Statista, Number of Apps Available in Leading App Stores as of July 2015. http://www.statista.com/statistics-/276623/number-of-apps-available-in-leading-app-stores/. Accessed 1 July 2015
Tech in Asia, 10 Alternative Android App Stores in China. https://www.techinasia.com/10-android-app-stores-china-2014-edition/. Accessed 1 July 2015
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)
Business Insider, Clickthrough Rate and Cost-per-click on Facebook for Selected Sectors. http://www.businessinsider.com.au/chart-of-the-day-facebook-ctr-cpc-per-sector-2011-1. Accessed 1 July 2015
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)
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)
Amit, G., Bonchi, F., Lakshmanan, L.V.S.: A data-based approach to social influence maximization. Proc. VLDB Endowment 5(1), 73–84 (2011)
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)
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)
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)
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)
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)
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)
Ma, C.: A guide to singular value decomposition for collaborative filtering. Technical report. http://www.csie.ntu.edu.tw/~r95007/thesis/svdnetflix/report/report.pdf. Accessed 1 July 2015
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)
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)
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)
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)
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)
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)
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)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Wang, Q. et al. (2015). A Novel APP Recommendation Method Based on SVD and Social Influence. In: Wang, G., Zomaya, A., Martinez, G., Li, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2015. Lecture Notes in Computer Science(), vol 9529. Springer, Cham. https://doi.org/10.1007/978-3-319-27122-4_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-27122-4_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27121-7
Online ISBN: 978-3-319-27122-4
eBook Packages: Computer ScienceComputer Science (R0)