Abstract
There has been high influx of data over the Web in the past few decades. In order to convert this data into meaningful information, data analytics platforms such as big data analytics have emerged. Recommendation system (RS) is one such analytic system which uses predictive analysis to present and target the useful information to the users in order to help them arriving at desired conclusions. RS saves the time of most of the Internet users by reducing the number of actions needed to get to the required information which helps in effective decision making. RS has been widely used in multiple domains such as e-commerce, social networks to present recommended options to users in their current context. Recommended options thereby presented are based on the action taken by other users in similar context. RS is equally useful in healthcare services. In fact, they are a complementary tool to healthcare system which allows for effective decision making in healthcare services. RS uses either collaborative filtering approach, content filtering approach, or a blend of them (hybrid approach). This paper discusses the overview of what recommender systems are, how they are built, and its classifications. It also elaborates health recommender system (HRS) and gives a clear picture of how MapReduce Framework and Hadoop technology will help in improving the scalability and efficiency of HRS by stating illustrations.
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References
Saravanan, S.: Design of large-scale content-based recommender system using Hadoop MapReduce framework. In: IC3, IEEE Computer Society, pp. 302–307 (2015). doi:10.1109/IC3.2015.7346697
Rakesh, et al.: Comparison of SQL with Hive QL. Int. J. Res. Technol. Stud. 1(9), 2348–1439 (2014)
Chandan, V., et al.: Increasing efficiency of recommendation system using big data analytics. Int. J. Recent Innov. Trends Comput. Commun. 3(10), 5742–5744 (2014)
Prem, M., Vikas, S.: Recommender system. In: Encyclopedia of Machine Learning, pp. 829–838. Springer, US (2011)
Adomavicius, G., Tuzhilin, A.: Towards the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Data Knowl. Eng. 17(6), 734–749 (2005)
Ghodake, S.B., Paswan, R.S.: Survey on recommender system using distributed frame work. Int. J. Sci. Res. (IJSR) 5(1) (2016). ISSN 2319–7064
Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, WI (1998)
Pereira, N., et al.: Survey on content based recommendation system. Int. J. Comput. Sci. Inf. Technol. 7(1), 281–284 (2016)
Balabanovic, M., Shoham, Y.: Content-based, collaborative recommendation. Commun. Assoc. Comput. Mach. 40(3), 66–72(1997). doi:10.1145/245108.245124
Zhang, Q., et al.: A framework of hybrid recommender system for personalized clinical prescription. In: International Conference on Intelligent Systems and Knowledge Engineering, pp. 189–195. IEEE (2015)
Pazzani, M.J., Billsus, D.: Learning and revising user profiles: the identification of interesting web sites. Mach. Learn. 27(3), 313–331 (1997). doi:10.1023/A:1007369909943
Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adap. Inter. 12(4), 331–370 (2002). doi:10.1023/A:1021240730564
Lamber, P., Girardello, A., et al.: MobiDay: a personalized context-aware mobile service for day hospital workflow support. In: Proceedings of the AIME09 International Workshop on Personalization for e-Health, pp. 15–19 (2009)
Kim, J.H., Lee, J.H., et al.: Design of diet recommendation system for healthcare service based on user information. In: Fourth International Conference on Computer Sciences and Convergence Information Technology, pp. 516–518. ICCIT (2009)
Duan, L., Street, W.N., Xu, E.: Healthcare information systems: data mining methods in the creation of a clinical recommender system. EIS 5(2), 169–181 (2011)
Huang, Y.F.: A doctor recommendation algorithm based on doctor performances and patient preferences. In: International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP), pp. 92–95 (2012)
Hussein, A.S., Omar, W.M., Li, X., Ati, M.: Efficient chronic disease diagnosis prediction and recommendation system. In: Biomedical Engineering and Sciences (IECBES), IEEE EMBS Conference, pp. 209–214 (2012). doi:10.1109/IECBES.2012.6498117
Chawla, N.V., Dawis, D.: Bringing big data to personalized healthcare: a patient centered framework. J. Gen. Intern. Med. 28, 660–665 (2013)
Mohammadi, N., Babaei, M.H.: Recommending an appropriate doctor to a patient based on fuzzy logic. Int. J. Curr. Life Sci. 4(2), 403–407 (2014)
Narducci, F., Musto, C., et al.: A recommender system for connecting patients to the right doctors in the HealthNet Social Network. WWW (Companion Volume), pp. 81–82. ACM (2015)
Ambika, M., et al.: Hybrid model for behaviour-based recommender system: a healthcare perspective. Int. J. Adv. Eng. Technol. 7(3), 88–91 (2016)
Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)
Vinodhini, S., Rajalakshmi, V., Govindarajalu, B.: Building personalised recommendation system with big data and hadoop MapReduce. Int. J. Eng. Res. Technol. (IJERT) 3(4) (2014). ISSN 2278–0181
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Bateja, R., Dubey, S.K., Bhatt, A. (2018). Health Recommender System and Its Applicability with MapReduce Framework. In: Pant, M., Ray, K., Sharma, T., Rawat, S., Bandyopadhyay, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 584. Springer, Singapore. https://doi.org/10.1007/978-981-10-5699-4_25
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