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Blockchain Technology for Mobile Applications Recommendation Systems

모바일앱 추천시스템과 블록체인 기술

  • Umekwudo, Jane O. (Department of Computer Science, Sookmyung Women's University) ;
  • Shim, Junho (Department of Computer Science, Sookmyung Women's University)
  • Received : 2019.07.23
  • Accepted : 2019.08.07
  • Published : 2019.08.31

Abstract

The interest in the blockchain technology has been increasing since its inception and it has been applied to many fields and sectors. The blockchain technology creates a decentralized environment where no third party controls the data and transaction. Mobile apps recommendation has been extensively used to recommend apps to mobile users. For example, Android-based recommendation applications have been developed to recommend other mobile apps for download depending on user's preferences and mobile context. These recommendations help users discover apps by referring to the experiences of other users. Due to the collection of a large amount of data and user information, there is a problem of insecurity and user's privacy that are prone to be attacked. To address this issue the blockchain technology can be incorporated to assure cryptographic safety. In this paper, we present a survey of the on-going mobile app recommendations and e-commerce technology trend to address how the blockchain can be incorporated into the collaborative filtering recommendation systems to enable the users to set up a secured data, which implies the importance of user privacy preference on personalized app recommendations.

블록체인기술에 대한 관심은 지속적으로 증가되고 많은 분야에 활용되고 있다. 블록체인기술은 타인이 함부로 데이터와 거래를 제어할 수 없게 하는 분산 환경을 제공한다. 모바일앱 추천은 모바일 사용자에게 적당한 앱을 추천하는데 사용된다. 예를 들어, 사용자의 선호도 및 모바일 환경에 따라 서로 다른 모바일앱을 추천하는 복수의 안드로이드기반 추천앱이 개발되어왔다. 앱 추천은 사용자가 다른 사용자의 경험을 참조하여 앱을 발견하는 데 도움을 준다. 수집된 많은 양의 데이터 및 사용자 정보는 외부 공격에 대한 취약성과 사용자 개인 정보 보호 문제를 내포한다. 이 문제를 해결하는 방법으로 암호화 안전을 보장하는 블록체인 기술을 적용할 수 있다. 본 서베이 논문에서는 모바일앱 추천 기술과 전자상거래 기술 동향을 살펴본다. 개인화된 앱 추천에 대한 사용자의 개인 정보 선호 중요성 측면에서, 블록체인기술과 협업필터링 기술의 접목이 사용자에게 안전한 데이터 환경을 제공할 수 있는지도 살펴본다.

Keywords

References

  1. Ameer, R., "5 Blockchain applications that Are shaping your future," https://www.huffpost.com/entry/5-blockchain-applications_b_13279010, 2017.
  2. Appfire, "madvertise - from http://www.appsfire.com, 2019.
  3. AppBrain, "Monetize, advertise and analyze Android apps," http://www.appbrain.com, 2019.
  4. Appspace, "A Software Platform for the Modern Workplace," https://www.appspace.com, 2019.
  5. Bohme, M., Bauer, G., and Kruger, A., "Exploring the design space of context-aware recommender systems that suggest mobile applications," in Proceedings of CARS, 2010.
  6. Breese, J., Heckerman, D., and Kadie, C., "Empirical analysis of predictive algorithms for collaborative filtering," Proceedings of Uncertainty in Artificial Intelligence, 1998.
  7. Choi, S. S. and Choi, M. K., "Consumer's privacy concerns and willingness to provide personal information in locationbased services. Advanced Communication Technology," The 9th International Conference on, pp. 2196-2199, 2007.
  8. Chen, L., Hsu, F., Chen, M., and Hsu, Y., “Developing recommender systems with the consideration of product profitability for sellers,” Information Sciences, Vol. 178, No. 4, pp. 1032-1048, 2008. https://doi.org/10.1016/j.ins.2007.09.027
  9. Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., and Sartin, M., "Combining content- based and collaborative filters in an online newspaper," Proceedings of ACM SIGIR workshop on recommender systems: algorithms and evaluation, Berkeley, California, 1999.
  10. Cotter, P., and Smyth, B., "PTV: Intelligent personalized TV guides," In: Twelfth conference on innovative applications of artificial intelligence, pp. 957-964, 2000.
  11. Ozmen, M. and Yucel, E., "Handling of online information by users: evidence from TED talks," Behaviour & Information Technology, pp. 1-15, 2019.
  12. Deshpande, M. and Karypis, G., “Itembased top-N recommendation algorithms,” ACM Transactions On Information Systems, Vol. 22, No. 1, pp. 143-177, 2004. https://doi.org/10.1145/963770.963776
  13. Ricci, F., “Mobile Recommender Systems,” Information Technology & Tourism, Vol. 12, No. 3, pp. 205-231, 2010. https://doi.org/10.3727/109830511X12978702284390
  14. Frey, R., Ilic, A., and Worner, D., "Collaborative Filtering on the Blockchain: A Secure Recommender System for e-Commerce," Collaborative Filtering on the Blockchain, pp. 3-4, 2016.
  15. Goldberg, D., Nichols, D., Oki, B., and Terry, D., “Using collaborative filtering to weave an information tapestry,” Communications of the ACM, Vol. 35, No. 12, pp. 61-70, 1992. https://doi.org/10.1145/138859.138867
  16. Girardello, A. and Michahelles, F., "AppAware: which mobile applications are hot?," Proceedings of MobileHCI '10, pp. 431-434, 2010.
  17. Gurpreet, S. and Rajdavinder, S., “A survey on recommendation system,” IOSR, Journal of Computer Engineering, Vol. 17, No. 6, pp. 46-51, 2015.
  18. Lifewire, Viswanathan, P., "What's a Mobile App?," https://www.lifewire.com/what-is-a-mobile-application-2373354, 2017.
  19. Mahmood, T. and Ricci, F., "Improving Recommender Systems with Adaptive Conversational Strategies," Proceedings of the 20th ACM conference on Hypertext and hypermedia, pp. 73-82, 2009.
  20. Pazzani, M., "A framework for collaborative, content-based and demographic filtering," Artificial Intelligence Review, Vol. 13, pp. 393-408, 1999. https://doi.org/10.1023/A:1006544522159
  21. Parameswaran, S., Luo, E., and Nguyen, T., “Patch Matching for Image Denoising Using Neighborhood-Based Collaborative Filtering,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 28, No. 2, pp. 392-401, 2018. https://doi.org/10.1109/TCSVT.2016.2610038
  22. Rafter, R. and Smyth, B., “Conversational Collaborative Recommendation: An Experimental Analysis,” Artificial Intelligence Review, Vol. 24, No. 3-4, pp. 301-318, 2005. https://doi.org/10.1007/s10462-005-9004-8
  23. Seebacher, S. and Schuritz, R., "Blockchain technology as an Enabler of Service System: A Structured Literature Review," International Conference on Exploring Services Science, pp. 12-23, 2017.
  24. Tilahun, B., Awono, C., and Batchakui, B., “A Survey of State-of-the-art: Deep Learning Methods on Recommender System,” International Journal of Computer Applications, Vol. 162, No. 10, pp. 17-22, 2017. https://doi.org/10.5120/ijca2017913361
  25. Umekwudo, J. and Shim, J., "How the Blockchain can be incorporated into the Collaborative Filtering Recommendation Systems," 2017 Fall Conference of KISM & SEBS, Society for e-Business Studies, 2017.
  26. Umekwudo, J., "A Survey of Recommender System for Mobile Application," M.S. Dissertation, Department of Computer Science, Sookmyung Women University, Seoul, 2017.
  27. Vekariya, V. and Kulkarni, G., "Hybrid Recommender Systems: Content-Boosted Collaborative Filtering for Improved Recommendations," Communication Systems and Network Technologies, International Conference, Vol. 1, pp. 649-653, 2012.
  28. Woerndl, W., Schueller, C., and Wojtech, R., "A Hybrid Recommender System for Context-aware Recommendations of Mobile Applications," IEEE 23rd International Conference On Data Engineering Workshop, pp. 871-878, 2007.
  29. Su, X. and Khoshgoftaar, T., "A Survey of Collaborative Filtering Techniques," Advances In Artificial Intelligence, pp. 1-19, 2009.
  30. Yan, B. and Chen, G., "Appjoy: personalized mobile application discovery," Proceedings of the 9th international conference on Mobile systems, applications, and services, ACM, pp. 113-126, 2011.
  31. Yixuan, Z. and Zhixiong, C., "Real ID: Building A Secure Anonymous Yet Transparent Immutable ID Service," IEEE 3rd International Conference on Big Data Security on Cloud, Beijing, China, 2017.
  32. Ziegler, C., McNee, S., Konstan, J., and Lausen, G., "Improving recommendation lists through topic diversification," Proceedings of the 14th international conference on World Wide Web, pp. 22-32, 2005.
  33. Zyskind, G., Nathan, O., and Pentland, A., "Enigma: Decentralized computation platform with guaranteed privacy, arXiv preprint arXiv:1506.03471, 2015.

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