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International conference on the Sciences of Electronics, Technologies of Information and Telecommunications

SETIT 2018: Proceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT’18), Vol.1 pp 182–191Cite as

Health Recommender Systems: A Survey

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Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 146))

Abstract

With the big amount of data that become available on the internet, and with the appearance of the information overload problem, it is becoming essential to use recommender systems (RS). RSs help users to extract relevant information that interests them, also to increase their quality decisions. These systems have proven their effectiveness in several domains, such as: e-commerce, e-learning, etc. Furthermore, they play a very important role in the field of medicine, in which the discovery of a tiny knowledge can save thousands of lives. In this paper, we will present a state of the art on RS approaches, their applications in general, and in the medical field.

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References

  1. Lerato, M., Esan, O.A., Ebunoluwa, A.-D., Ngwira, S., Zuva, T.: A survey of recommender system feedback techniques, comparison and evaluation metrics (2015)

    Google Scholar 

  2. 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, 734–749 (2005)

    Article  Google Scholar 

  3. Pazzani, M.J.: A framework for collaborative, content-based and demographic filtering. Artif. Intell. Rev. 13, 393–408 (1999)

    Article  Google Scholar 

  4. Asanov, D.: Algorithms and Methods in Recommender Systems. 7. Berlin Institute of Technology, Berlin, Germany (2011)

    Google Scholar 

  5. Lu, J., Wu, D., Mao, M., Wang, W., Zhang, G.: Recommender system application developments: a survey. Decis. Support. Syst. 74, 12–32 (2015)

    Article  Google Scholar 

  6. Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.): Recommender Systems Handbook. Springer, Boston (2011)

    MATH  Google Scholar 

  7. Bobadilla, J., Hernando, A., Ortega, F., Gutiérrez, A.: Collaborative filtering based on significances. Inf. Sci. 185, 1–17 (2012)

    Article  Google Scholar 

  8. Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowl. Based Syst. 46, 109–132 (2013)

    Article  Google Scholar 

  9. Shu, J., Shen, X., Liu, H., Yi, B., Zhang, Z.: A content-based recommendation algorithm for learning resources. Multimed. Syst. 24, 163–173 (2018)

    Article  Google Scholar 

  10. Wang, D., Liang, Y., Xu, D., Feng, X., Guan, R.: A content-based recommender system for computer science publications. Knowl. Based Syst. 157, 1–9 (2018)

    Article  Google Scholar 

  11. Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 1–19 (2009)

    Article  Google Scholar 

  12. Nilashi, M., Ibrahim, O., Bagherifard, K.: A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Syst. Appl. 92, 507–520 (2018)

    Article  Google Scholar 

  13. Korfiatis, N., Poulos, M.: Using online consumer reviews as a source for demographic recommendations: a case study using online travel reviews. Expert Syst. Appl. 40, 5507–5515 (2013)

    Article  Google Scholar 

  14. Zhao, W.X., Li, S., He, Y., Wang, L., Wen, J.-R., Li, X.: Exploring demographic information in social media for product recommendation. Knowl. Inf. Syst. 49, 61–89 (2016)

    Article  Google Scholar 

  15. Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adap. Inter. 12, 331–370 (2002)

    Article  Google Scholar 

  16. Hendrik, H., Azzakiy, K., Utomo, A.B.: Semantic hybrid recommender system. Adv. Sci. Lett. 21, 3363–3366 (2015)

    Article  Google Scholar 

  17. Pandya, S., Shah, J., Joshi, N., Ghayvat, H., Mukhopadhyay, S.C., Yap, M.H.: A novel hybrid based recommendation system based on clustering and association mining (2016)

    Google Scholar 

  18. Adomavicius, G.: Context-aware recommender systems. In: Recommender Systems Handbook, pp. 217–253. Springer, Boston (2011)

    MATH  Google Scholar 

  19. Xu, Z., Chen, L., Chen, G.: Topic based context-aware travel recommendation method exploiting geotagged photos. Neurocomputing 155, 99–107 (2015)

    Article  Google Scholar 

  20. Yilong, S., Hong, L., Yuqiang, M.H.: Context-Aware Recommender Systems Based on Item-Grain Context Clustering. Springer, Heidelberg (2017)

    Google Scholar 

  21. Peis, E., Morales-del-Castillo, J.M., Delgado-López, J.A.: Semantic recommender systems. Analysis of the state of the topic (2008)

    Google Scholar 

  22. Musto, C., Lops, P., de Gemmis, M., Semeraro, G.: Semantics-aware recommender systems exploiting linked open data and graph-based features. Knowl. Based Syst. 136, 1–14 (2017)

    Article  Google Scholar 

  23. Duran, D., Chanchí, G., Arciniegas, J.L., Baldassarri, S.: A semantic recommender system for iDTV based on educational competencies. In: Applications and Usability of Interactive TV, pp. 47–61. Springer, Cham (2017)

    Google Scholar 

  24. Capdevila, J., Arias, M., Arratia, A.: GeoSRS: a hybrid social recommender system for geolocated data. Inf. Syst. 57, 111–128 (2016)

    Article  Google Scholar 

  25. Pereira, C.K., Campos, F., Ströele, V., David, J.M.N., Braga, R.: BROAD-RSI – educational recommender system using social networks interactions and linked data. J. Internet Serv. Appl. 9, 7 (2018)

    Article  Google Scholar 

  26. Wiesner, M., Pfeifer, D.: Health recommender systems: concepts, requirements, technical basics and challenges. Int. J. Environ. Res. Public Health 11, 2580–2607 (2014)

    Article  Google Scholar 

  27. Calero Valdez, A., Ziefle, M., Verbert, K., Felfernig, A., Holzinger, A.: Recommender systems for health informatics: state-of-the-art and future perspectives. In: Machine Learning for Health Informatics, pp. 391–414 (2016)

    Google Scholar 

  28. Sezgin, E., Ozkan, S.: A systematic literature review on Health Recommender Systems (2013)

    Google Scholar 

  29. Bateja, R., Dubey, S.K., Bhatt, A.: Health recommender system and its applicability with MapReduce framework. In: Soft Computing: Theories and Applications, pp. 255–266. Springer, Singapore (2018)

    Google Scholar 

  30. Lopez-Nores, M., Blanco-Fernandez, Y., Jose, J.P.-A., Rebeca, P.D.-R.: Property-based collaborative filtering: a new paradigm for semantics-based, health-aware recommender systems (2010)

    Google Scholar 

  31. Zhang, Q., Zhang, G., Lu, J., Wu, D.: A framework of hybrid recommender system for personalized clinical prescription (2015)

    Google Scholar 

  32. Graber, F., Malberg, H., Zaunseder, S., Beckert, S., Kuster, D., Schmitt, J., Klinik, S.A., Dermatologie, P.: Application of recommender system methods for therapy decision support. In: IEEE 18th International Conference on e-Health Networking, Applications and Services (2016)

    Google Scholar 

  33. Hu, S., Lu, L., Jin, X., Jiang, Y., Zheng, H., Xu, Q., Cai, F., Meng, Y., Zhang, C.: The recommender system for a cloud-based electronic medical record system for regional clinics and health centers in China (2017)

    Google Scholar 

  34. Größer, F., Malberg, H., Zaunseder, S., Beckert, S., Küster, D., Schmitt, J., Abraham, S.: Neighborhood-based collaborative filtering for therapy decision support. In: 2nd International Workshop on Health Recommender Systems (2017)

    Google Scholar 

  35. Hu, J., Sharma, S., Gao, Z., Chang, V.: Gene-based Collaborative filtering using recommender system. Comput. Electr. Eng. 65, 332–341 (2018)

    Article  Google Scholar 

  36. Agu, E., Claypool, M.: Cypress: a cyber-physical recommender system to discover smartphone exergame enjoyment. In: Proceedings of the ACM Workshop on Engendering Health with Recommender Systems (2016)

    Google Scholar 

  37. Hors-Fraile, S., Benjumea, F.J.N., Hernández, L.C., Ruiz, F.O., Fernandez-Luque, L.: Design of two combined health recommender systems for tailoring messages in a smoking cessation app (2016). arXiv preprint: arXiv:1608.07192

  38. Manogaran, G., Varatharajan, R., Priyan, M.K.: Hybrid recommendation system for heart disease diagnosis based on multiple kernel learning with adaptive neuro-fuzzy inference system. Multimed. Tools Appl. 77(4), 4379–4399 (2018)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. RIADI Laboratory, National School of Computer Science, Manouba University, 2010, La Manouba, Tunisia

    Hafsa Lattar, Aïcha Ben Salem & Henda Hajjami Ben Ghézala

  2. Laboratory LIPN-UMR 7030-CNRS, University Paris 13, Sorbonne Paris Cité, Villetaneuse, France

    Faouzi Boufares

Authors
  1. Hafsa Lattar
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  2. Aïcha Ben Salem
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  3. Henda Hajjami Ben Ghézala
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  4. Faouzi Boufares
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Corresponding authors

Correspondence to Hafsa Lattar , Aïcha Ben Salem , Henda Hajjami Ben Ghézala or Faouzi Boufares .

Editor information

Editors and Affiliations

  1. SETIT Lab, University of Sfax, Sfax, Tunisia

    Med Salim Bouhlel

  2. DIBRIS - University of Genoa, Genova, Genova, Italy

    Stefano Rovetta

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Lattar, H., Ben Salem, A., Hajjami Ben Ghézala, H., Boufares, F. (2020). Health Recommender Systems: A Survey. In: Bouhlel, M., Rovetta, S. (eds) Proceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT’18), Vol.1. SETIT 2018. Smart Innovation, Systems and Technologies, vol 146. Springer, Cham. https://doi.org/10.1007/978-3-030-21005-2_18

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