Skip to main content

Advertisement

Springer Nature Link
Log in

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

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Notes

  1. http://www.tmall.com.

  2. www.walgreens.com.

References

  1. Bhanumathi V, Sangeetha CP (2017) A guide for the selection of routing protocols in WBAN for healthcare applications. Human Centric Comput Inf Sci 7:24

    Article  Google Scholar 

  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. https://doi.org/10.1016/j.future.2018.01.007

    Google Scholar 

  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 40–46

  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):157–172

    Article  Google Scholar 

  5. Toledo RY, Mota YC, Borroto MG (2013) A regularity-based preprocessing method for collaborative recommender systems. J Inf Process Syst 9(3):435–460

    Article  Google Scholar 

  6. Linden G, Smith B, York J (2003) Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput 7(1):76–80

    Article  Google Scholar 

  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 335–344

  8. Breese JS, Heckerman D, Kadie C (2013) Empirical analysis of predictive algorithms for collaborative filtering. Uncertainty Artif Intell 98(7):43–52

    Google Scholar 

  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):4290–4311

    Article  Google Scholar 

  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 216–223

  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 321–325

  12. Rafailidis D, Kefalas P, Manolopoulos Y (2017) Preference dynamics with multimodal user-item interactions in social media recommendation. Expert Syst Appl 74:11–18

    Article  Google Scholar 

  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 507–515

  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 324–328

  15. https://en.wikipedia.org/wiki/Tensor. Accessed 10 Mar 2018

  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):122–135

    Article  MathSciNet  Google Scholar 

  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 43–50

  18. Taneja A, Arora A (2017) Cross domain recommendation using multidimensional tensor factorization. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2017.09.042

    Google Scholar 

  19. https://en.wikipedia.org/wiki/Higherorder_singular_value_decomposition. Accessed 10 Mar 2018

  20. Nouy A (2016) Low-rank methods for high-dimensional approximation and model order reduction. Mathematics

  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 Databases

  22. Rovi A (2011) Analysis of 2*2*2 tensor. LiTH-MAT-INT-A-2010/01 SE. Linkopings University

  23. Baranyi P (2004) TP model transformation as a way to LMI based controller design. IEEE Trans Ind Electron 51(2):387–400

    Article  Google Scholar 

  24. https://en.wikipedia.org/wiki/SVD. Accessed 10 Mar 2018

  25. Coppersmith D, Winograd S (1990) Matrix multiplication via arithmetic progressions. J Symb Comput 9(3):251–280

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

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).

Author information

Authors and Affiliations

  1. Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi’an, China

    Fei Hao & Xiaoming Wang

  2. School of Computer Science, Shaanxi Normal University, Xi’an, China

    Fei Hao & Xiaoming Wang

  3. Department of Computer Software Engineering, Soonchunhyang University, Asan, South Korea

    Doo-Soon Park

  4. School of Information Science and Technology, Northwest University, Xi’an, China

    Xiaoyan Yin

  5. Department of Computer Science and Engineering, Soonchunhyang University, Asan, South Korea

    Vilakone Phonexay

Authors
  1. Fei Hao
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Doo-Soon Park
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Xiaoyan Yin
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Xiaoming Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Vilakone Phonexay
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Doo-Soon Park.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hao, F., Park, DS., Yin, X. et al. A location-sensitive over-the-counter medicines recommender based on tensor decomposition. J Supercomput 75, 1953–1970 (2019). https://doi.org/10.1007/s11227-018-2314-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-018-2314-9

Keywords