The Journal of Supercomputing

, Volume 75, Issue 4, pp 1953–1970 | Cite as

A location-sensitive over-the-counter medicines recommender based on tensor decomposition

  • Fei Hao
  • Doo-Soon ParkEmail author
  • Xiaoyan Yin
  • Xiaoming Wang
  • Vilakone Phonexay


The last few decades have witnessed a steady increase in medicine prescriptions for the treatment of biometric markers rather than obvious physiological symptoms; especially, the over-the-counter (OTC) medicine experiences rated by patients have huge potential to assist people to make more appropriate decisions. The most existing researches focus on the rating prediction and recommendations in E-commerce field rather than healthcare or medical treatments. In addition, the spatial and temporal factors were not considered in their recommendation mechanisms. Toward this end, this paper propose an efficient OTC medicines recommendation strategy based on tensor decomposition. Considering the impact of regional differentiation, a third-order tensor including medicine, location, and rating is constructed. To inference the usage of a new OTC medicine in a certain location, high-order singular value decomposition is applied to the above tensor for obtaining the intelligent recommendation. In order to evaluate the effectiveness of the proposed approach, we compared the conventional collaborative filtering approach and tensor-based approach in terms of precision and recall. The experimental results demonstrate that our proposed approach is significant better than collaborative filtering approach.


Medicines recommendation Tensor decomposition HOSVD Dimensionality reduction Regional differentiation 



This research was supported by the National Natural Science Foundation of China (Grant Nos. 61702317, 61771297) and MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-2014-0-00720) supervised by the IITP (Institute for Information & communications Technology Promotion) and the National Research Foundation of Korea (No. NRF-2017R1A2B1008421) and was also supported by the Fund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shaanxi Province (Grant No. 2017024) as well as the Fundamental Research Funds for the Central Universities (GK201703059, GK201802013).


  1. 1.
    Bhanumathi V, Sangeetha CP (2017) A guide for the selection of routing protocols in WBAN for healthcare applications. Human Centric Comput Inf Sci 7:24CrossRefGoogle Scholar
  2. 2.
    Lin Y, Wang X, Hao F, Wang L, Zhang L, Zhao R (2018) An on-demand coverage based self-deployment algorithm in mobile wireless sensor networks. Future Gener Comput Syst. Google Scholar
  3. 3.
    Dou Y, Yang H, Deng X (2017) A survey of collaborative filtering algorithms for social recommender systems. In: Proceedings of the IEEE International Conference on Semantics, Knowledge and Grids, pp 40–46Google Scholar
  4. 4.
    Jeong WH, Kim SJ, Park DS, Kwak J (2013) Performance improvement of a movie recommendation system based on personal propensity and secure collaborative filtering. J Inf Process Syst 9(1):157–172CrossRefGoogle Scholar
  5. 5.
    Toledo RY, Mota YC, Borroto MG (2013) A regularity-based preprocessing method for collaborative recommender systems. J Inf Process Syst 9(3):435–460CrossRefGoogle Scholar
  6. 6.
    Linden G, Smith B, York J (2003) recommendations: item-to-item collaborative filtering. IEEE Internet Comput 7(1):76–80CrossRefGoogle Scholar
  7. 7.
    Chen J, Zhang H, He X, Nie L, Liu W, Chua TS (2017) Attentive collaborative filtering: multimedia recommendation with item- and component-level attention. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 335–344Google Scholar
  8. 8.
    Breese JS, Heckerman D, Kadie C (2013) Empirical analysis of predictive algorithms for collaborative filtering. Uncertainty Artif Intell 98(7):43–52Google Scholar
  9. 9.
    Barragns-Martinez AB, Costa-Montenegro C, Burguillo JC (2010) A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition. Inf Sci 180(22):4290–4311CrossRefGoogle Scholar
  10. 10.
    Li S, Hao F, Li M, Kim H (2013) Medicine rating prediction and recommendation in mobile social networks. In: Proceedings of The 8th International Conference on Grid and Pervasive Computing, vol 7861, pp 216–223Google Scholar
  11. 11.
    Fikir OB, Yaz IO, Ozyer T (2010) A movie rating prediction algorithm with collaborative filtering. In: Proceedings of International Conference on Advances in Social Networks Analysis and Mining, pp 321–325Google Scholar
  12. 12.
    Rafailidis D, Kefalas P, Manolopoulos Y (2017) Preference dynamics with multimodal user-item interactions in social media recommendation. Expert Syst Appl 74:11–18CrossRefGoogle Scholar
  13. 13.
    Sneha S, Jayalakshmi DS (2014) Recommending music by combining content-based and collaborative filtering with user preferences. In: Emerging Research in Electronics, Computer Science and Technology, pp 507–515Google Scholar
  14. 14.
    Kim HN, Ji AT, Yeon C, Jo GS (2007) A user-item predictive model for collaborative filtering recommendation. In: User Modeling. Springer, Berlin, pp 324–328Google Scholar
  15. 15.
  16. 16.
    Yu L, Huang J, Zhou G, Liu C, Zhang Z (2017) TIIREC: a tensor approach for tag-driven item recommendation with sparse user generated content. Inf Sci 411(10):122–135MathSciNetCrossRefGoogle Scholar
  17. 17.
    Symeonidis P, Nanopoulos A, Manolopoulos Y (2008) Tag recommendations based on tensor dimensionality reduction. In: Proceedings of the 2nd ACM International Conference on Recommender Systems, vol 296, pp 43–50Google Scholar
  18. 18.
    Taneja A, Arora A (2017) Cross domain recommendation using multidimensional tensor factorization. Expert Syst Appl. Google Scholar
  19. 19.
  20. 20.
    Nouy A (2016) Low-rank methods for high-dimensional approximation and model order reduction. MathematicsGoogle Scholar
  21. 21.
    Silva E, Langseth H, Ramampiaro H (2017) Content-based social recommendation with poisson matrix factorization. In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in DatabasesGoogle Scholar
  22. 22.
    Rovi A (2011) Analysis of 2*2*2 tensor. LiTH-MAT-INT-A-2010/01 SE. Linkopings UniversityGoogle Scholar
  23. 23.
    Baranyi P (2004) TP model transformation as a way to LMI based controller design. IEEE Trans Ind Electron 51(2):387–400CrossRefGoogle Scholar
  24. 24. Accessed 10 Mar 2018
  25. 25.
    Coppersmith D, Winograd S (1990) Matrix multiplication via arithmetic progressions. J Symb Comput 9(3):251–280MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Fei Hao
    • 1
    • 2
  • Doo-Soon Park
    • 3
    Email author
  • Xiaoyan Yin
    • 4
  • Xiaoming Wang
    • 1
    • 2
  • Vilakone Phonexay
    • 5
  1. 1.Key Laboratory of Modern Teaching TechnologyMinistry of EducationXi’anChina
  2. 2.School of Computer ScienceShaanxi Normal UniversityXi’anChina
  3. 3.Department of Computer Software EngineeringSoonchunhyang UniversityAsanSouth Korea
  4. 4.School of Information Science and TechnologyNorthwest UniversityXi’anChina
  5. 5.Department of Computer Science and EngineeringSoonchunhyang UniversityAsanSouth Korea

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