Abstract
Recommender systems mainly utilize for finding and recover contents from large datasets; it has been determining and analysis based on the scenario—Big Data. In this paper, we describe the process of recommendation system using big data with a clear explanation in representing the operation of mapreduce. We demonstrate the various stage of recommendation namely data collection rating, types of filtering. Analysis Scenario based drug recommender system, it consists of three components namely drug storage, cloud server, and recommender server. The system is evaluating with specific parameters like F-score, Precision, and recall. Finally, we describe the challenge of recommendation systems like data sparsity, cold start, sentimental analysis and No surprise.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bhosale, H.S., Gadekar, D.P.: A review paper on big data and Hadoop. Int. J. Sci. Res. Publ. 4(10), 1–7 (2014)
Verma, J.P., Patel, B., Patel, A.: Big data analysis: recommendation system with Hadoop framework. In: 2015 IEEE International Conference on Computational Intelligence and Communication Technology (CICT), pp. 92–97. IEEE, Feb 2015
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)
Zhao, Z.D., Shang, M.S.: User-based collaborative-filtering recommendation algorithms on Hadoop. In: Third International Conference on Knowledge Discovery and Data Mining, 2010. WKDD’10, pp. 478–481. IEEE, Jan 2010
Patil, S.N., Deshpande, S.M., Potgantwar, A.D.: Product recommendation using multiple filtering mechanisms on Apache spark (2017)
Isinkaye, F.O., Folajimi, Y.O., Ojokoh, B.A.: Recommendation systems: principles, methods, and evaluation. Egypt. Inf. J. 16(3), 261–273 (2015)
Chen, R.C., Huang, Y.H., Bau, C.T., Chen, S.M.: A recommendation system based on domain ontology and SWRL for anti-diabetic drugs selection. Expert Syst. Appl. 39(4), 3995–4006 (2012)
Wang, H., Gu, Q., Wei, J., Cao, Z., Liu, Q.: Mining drug-disease relationships as a complement to medical genetics-based drug repositioning: where a recommendation system meets genome-wide association studies. Clin. Pharmacol. Ther. 97(5), 451–454 (2015)
Jamiy, F.E., Daif, A., Azouazi, M., Marzak, A.: The potential and challenges of Big data-Recommendation systems next level application. arXiv preprint arXiv: 1501.03424 (2015)
Wali, M.N., Sree Prasanna, K., Surabhi, L.: An optimistic analysis of big data by using HDFS
Dhavapriya, M., Yasodha, N.: Big data analytics: challenges and solutions using Hadoop, map reduce and big table. Int. J. Comput. Sci. Trends Technol. (IJCST) 4(1) (2016)
Bollier, D., Firestone, C.M.: The promise and peril of big data, p. 56. Aspen Institute, Communications, and Society Program, Washington, DC (2010)
Helbing, D., Frey, B.S., Gigerenzer, G., Hafen, E., Hagner, M., Hofstetter, Y., Zwitter, A.: Will democracy survive big data and artificial intelligence? Scientific American. 25 Feb 2017
Azar, A.T., Vaidyanathan, S. (eds.): Advances in Chaos Theory and Intelligent Control, vol. 337. Springer (2016)
Wiesner, M., Pfeifer, D.: Health recommender systems: concepts, requirements, technical basics, and challenges. Int. J. Environ. Res. Public Health 11(3), 2580–2607 (2014)
Yang, S., Zhou, P., Duan, K., Hossain, M.S., Alhamid, M.F.: emHealth: towards emotion health through depression prediction and intelligent health recommender system. Mob. Netw. Appl. 1–11 (2017)
Holzinger, A.: Machine learning for health informatics. In: Machine Learning for Health Informatics, pp. 1–24. Springer International Publishing (2016)
Aznoli, F., Navimipour, N.J.: Cloud services recommendation: reviewing the recent advances and suggesting the future research directions. J. Netw. Comput. Appl. 77, 73–86 (2017)
Sharma, S.K., Suman, U.: An efficient semantic clustering of URLs for web page recommendation. Int. J. Data Anal. Tech. Strat. 5(4), 339–358 (2013)
Ma, H., Yang, H., Lyu, M.R., King, I.: Sorec: social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, pp. 931–940. ACM, Oct 2008
Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering for improved recommendations. In: Aaai/iaai, pp. 187–192, July 2002
Schein, A.I., Popescul, A., Ungar, L.H., Pennock, D.M.: Methods and metrics for cold-start recommendations. In: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 253–260. ACM, Aug 2002
Sagiroglu, S., Sinanc, D.: Big data: a review. In: 2013 International Conference on Collaboration Technologies and Systems (CTS), pp. 42–47. IEEE, May 2013
Das, A.S., Datar, M., Garg, A., Rajaram, S.: Google news personalization: scalable online collaborative filtering. In: Proceedings of the 16th International Conference on World Wide Web, pp. 271–280. ACM, May 2007
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sandeep Kumar, M., Prabhu, J. (2019). A Case Study on Recommendation Systems Based on Big Data. In: Satapathy, S., Bhateja, V., Das, S. (eds) Smart Intelligent Computing and Applications . Smart Innovation, Systems and Technologies, vol 105. Springer, Singapore. https://doi.org/10.1007/978-981-13-1927-3_44
Download citation
DOI: https://doi.org/10.1007/978-981-13-1927-3_44
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1926-6
Online ISBN: 978-981-13-1927-3
eBook Packages: EngineeringEngineering (R0)