Skip to main content
SpringerLink
Log in

Recommendation system techniques and related issues: a survey

  • Original Research
  • Published:
International Journal of Information Technology Aims and scope Submit manuscript

Abstract

Nowadays, e-commerce websites are emerging as a new market and allow the millions of product to the user for sale. The selection of product from millions of product requires an additional tool called recommendation system. The recommendation system (RS) helps the user to find the items they are looking for. Collaborative filtering is one of the techniques used in the RSs that is widely studied and used to make recommendation. In this paper, a review of the various methods, algorithms used in the recommender system, the metrics used in RSs and the challenges of recommendation system such as Cold-start, Data sparsity, Scalability, Privacy etc. have been discussed.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  1. Deshpande M, Karypis G (2004) Item-based top-N recommendation algorithms. ACM Trans Inf Syst 22(1):143–177

    Article  Google Scholar 

  2. Sarwar B, Karypis G, Konstan J, Riedl J (2000) Analysis of recommendation algorithms for e-commerce. In: Proceedings of the 2nd ACM conference on Electronic and commerce, pp 158–167

  3. Miller BN, Albert I, Lam SK, Konstan JA, Riedl J (2003) Movie Lens unplugged: experiences with an occasionally connected recommender system. In: Proceedings of the Int’l Conf. Intelligent user interfaces, Miami, Florida, USA, pp 223–266

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

    Article  Google Scholar 

  5. Goldberg K, Roeder T, Gupta D, Perkins C (2001) Eigentaste: a constant time collaborative filtering algorithm. Inf Retr J 4:133–151

    Article  MATH  Google Scholar 

  6. Rodrigues CM, Rathi S, Patil G (2016) An efficient system using item and user-based CF techniques to improve recommendation. In: Proc. of International Conf. on next generation computing technologies (NGCT), pp 569–574

  7. Ji K, Shen H (2014) Using category and keyword for personalized recommendation: a scalable collaborative filtering algorithm. In: Proc. of sixth international symposium on parallel architectures, algorithms and programming, Beijing, pp 197–202

  8. Gu L, Yang P, Dong Y (2014) An dynamic-weighted collaborative filtering approach to address sparsity and adaptivity issues. In: 2014 IEEE Congress on Evolutionary Computation (CEC), Beijing, pp 3044–3050

  9. Koohi H, Kiani K (2017) A new method to find neighbor users that improves the performance of collaborative filtering. Expert Syst Appl 83:30–39

    Article  Google Scholar 

  10. Verma SK, Mittal N, Agarwal B (2013) Hybrid recommender system based on fuzzy clustering and collaborative filtering. In: Proceedings of the 4th International Conference on Computer and Communication Technology (ICCCT), Allahabad, pp 116–120

  11. Nitin PK, Fan Z (2015) Hybrid user-item based collaborative filtering. Procedia Comput Sci 60:1453–1461

    Article  Google Scholar 

  12. Koohi H, Kiani K (2016) User based Collaborative Filtering using fuzzy C-means. Measurement 91:134–139

    Article  Google Scholar 

  13. Lee OJ, Jung JJ, Eunsoon Y (2015) Predictive clustering for performance stability in collaborative filtering techniques. In: Proceedings of the IEEE 2nd International Conference on Cybernetics (CYBCONF), Gdynia, 2015, pp 48–55

  14. Kim K-J, Ahn H (2008) A recommender system using GA -means clustering in an online shopping market. Expert Syst Appl 34(2):1200–1209

    Article  Google Scholar 

  15. Ar Y, Bostanci E (2016) A genetic algorithm solution to the collaborative filtering problem. Expert Syst Appl 61:122–128

    Article  Google Scholar 

  16. Kumar B, Sharma N (2016) Approaches, issues and challenges in recommender systems: a systematic review. Ind J Sci Technol 9(47). https://doi.org/10.17485/ijst/2016/v9i47/94892

  17. Isinkaye FO, Folajimi YO, Ojokoh BA (2015) Recommendation systems: principles, methods and evaluation. Egypt Inform J 16:261–273

    Article  Google Scholar 

  18. Khusro S, Ali Z, Ullah I (2016) Recommender systems: issues, challenges, and research opportunities. In: Kim K, Joukov N (eds) Information science and applications (ICISA) 2016. Lecture Notes in Electrical Engineering, vol 376. Springer, Singapore, pp 1179–1189

  19. Sharma L, Gera A (2013) A survey of recommendation system: research challenges. Int J Eng Trends Technol 4(5):1989–1992

    Google Scholar 

  20. Gong S (2010) A collaborative filtering recommendation algorithm based on user clustering and item clustering. J Softw 5(7):745–752

    Article  Google Scholar 

  21. Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)?—arguments against avoiding RMSE in the literature. Geosci Model Dev Discuss 7:1247–1250

    Article  Google Scholar 

  22. Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst (TOIS) 22(1):5–53

    Article  Google Scholar 

  23. Yang Z, Wu B, Zheng K, Wang X, Lei L (2016) A survey of collaborative filtering-based recommender systems for mobile internet applications. IEEE Access 4:3273–3287

    Article  Google Scholar 

  24. Adomavicius G, Tuzhilin A (2005) 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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Applications, Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, India

    Pushpendra Kumar & Ramjeevan Singh Thakur

Authors
  1. Pushpendra Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ramjeevan Singh Thakur
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pushpendra Kumar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, P., Thakur, R.S. Recommendation system techniques and related issues: a survey. Int. j. inf. tecnol. 10, 495–501 (2018). https://doi.org/10.1007/s41870-018-0138-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41870-018-0138-8

Keywords

Search

Navigation