Skip to main content
SpringerLink
Log in

A novel collaborative filtering based recommendation system using exponential grasshopper algorithm

  • Research Paper
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

Collaborative filtering recommends items on the principle of similarity. The effectiveness of the similarity is highly dependent on the performance of the clustering algorithm. This paper presents a new clustering-based collaborative filtering method for efficient item recommendation. The new approach introduces a new variant of the grasshopper optimization algorithm to determine optimal similarity patterns. Nine nature-inspired algorithms have been considered and tested against seventeen standard benchmark functions in terms of mean fitness value and the Friedman test to validate the proposed variant experimentally. Further, the efficacy of the new clustering-based collaborative filtering method is tested on the publicly available MovieLens dataset and evaluated in terms of mean absolute error, precision, and recall over the different cluster settings. The obtained results are compared against nine clustering-based collaborative filtering methods. Experiments affirm that the proposed method is a permissive technique for the recommendation on large-scale datasets.

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
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Fu S, Yan Q, Feng GC (2018) Who will attract you? similarity effect among users on online purchase intention of movie tickets in the social shopping context. Int J Inf Manag 40:88–102

    Article  Google Scholar 

  2. Hartanto M, Utama DN (2020) Intelligent decision support model for recommending restaurant. Cogent Eng 7(1):1763888

    Article  Google Scholar 

  3. Pazzani MJ, Billsus D (2007) Content-based recommendation systems. In: The adaptive web. Springer, pp. 325–341

  4. Ahmadi A, Mukherjee D, Ruhe G (2019) A recommendation system for emergency mobile applications using context attributes: Remac. In: Proceedings of the 3rd ACM SIGSOFT international workshop on app market analytics, pp. 1–7

  5. Ramakrishnan G, Saicharan V, Chandrasekaran K, Rathnamma M, Ramana VV (2020) Collaborative filtering for book recommendation system. In: Soft computing for problem solving. Springer, pp. 325–338

  6. Deebak B, Al-Turjman F (2020) Covid-19 patient care: a content-based collaborative filtering using intelligent recommendation system. In: International summit smart city 360. Springer, pp. 31–44

  7. Gupta M, Thakkar A, Gupta V, Rathore DPS et al. (2020) Movie recommender system using collaborative filtering. In: 2020 International conference on electronics and sustainable communication systems (ICESC). IEEE, pp. 415–420

  8. Revankar OS, Haribhakta Y (2015) Survey on collaborative filtering technique in recommendation system. Int J Appl Innov Eng Manag 4(3):85–91

    Google Scholar 

  9. Suganeshwari G, Ibrahim SS (2016) A survey on collaborative filtering based recommendation system. In: Proceedings of the 3rd international symposium on big data and cloud computing challenges (ISBCC–16). Springer, pp. 503–518

  10. Khalid W, Xing X, Julius A, Niu Y, Tahir O, Ihsan I (2020) Efficiency analysis of collaborative based recommendation system. J Phys Conf Ser 1646(1):012150

    Article  Google Scholar 

  11. Mittal H, Saraswat M, Pal R (2020) Histopathological image classification by optimized neural network using igsa. In: International conference on distributed computing and internet technology. Springer, pp. 429–436

  12. Mittal H, Saraswat M (2018) ckgsa based fuzzy clustering method for image segmentation of rgb-d images. In: 2018 Eleventh international conference on contemporary computing (IC3). IEEE, pp. 1–6

  13. Mittal H, Saraswat M (2019) Classification of histopathological images through bag-of-visual-words and gravitational search algorithm. In: Soft computing for problem solving. Springer 231–241

  14. Tripathi AK, Mittal H, Saxena P, Gupta S (2020) A new recommendation system using map-reduce-based tournament empowered whale optimization algorithm. Complex Intell Syst 7:297

    Article  Google Scholar 

  15. Mittal H, Saraswat M (2018) An image segmentation method using logarithmic kbest gravitational search algorithm based superpixel clustering. Evolut Intell 14:1293

    Article  Google Scholar 

  16. Katarya R, Verma OP (2018) Recommender system with grey wolf optimizer and fcm. Neural Comput Appl 30(5):1679–1687

    Article  Google Scholar 

  17. Mittal H, Pal R, Kulhari A, Saraswat M (2016) “Chaotic kbest gravitational search algorithm (ckgsa). In: 2016 Ninth international conference on contemporary computing (IC3). IEEE, pp. 1–6

  18. Michalewicz Z, Janikow CZ (1991) Genetic algorithms for numerical optimization. Stat Comput 1(2):75–91

    Article  Google Scholar 

  19. Mittal H, Tripathi A, Pandey AC, Pal R (2020) Gravitational search algorithm: a comprehensive analysis of recent variants. Multimedia Tools Appl 80:7581

    Article  Google Scholar 

  20. Kulhari A, Pandey A, Pal R, Mittal H (2016) Unsupervised data classification using modified cuckoo search method. In: 2016 Ninth international conference on contemporary computing (IC3). IEEE, pp. 1–5

  21. Cutolo A, De Nicola C, Manzo R, Rarità L (2012) Optimal paths on urban networks using travelling times prevision. Modell Simul Eng. https://doi.org/10.1155/2012/564168

    Article  Google Scholar 

  22. Tripathi AK, Sharma K, Bala M, Kumar A, Menon VG, Bashir AK (2020) A parallel military dog based algorithm for clustering big data in cognitive industrial internet of things. IEEE Trans Ind Info 17:2134

    Article  Google Scholar 

  23. Pandey AC, Rajpoot DS, Saraswat M (2017) Twitter sentiment analysis using hybrid cuckoo search method. Inf Process Manag 53(4):764–779

    Article  Google Scholar 

  24. Cascone A, Manzo R, Piccoli B, Rarità L (2008) Optimization versus randomness for car traffic regulation

  25. Mittal H, Saraswat M (2018) An optimum multi-level image thresholding segmentation using non-local means 2d histogram and exponential kbest gravitational search algorithm. Eng Appl Artif Intell 71:226–235

    Article  Google Scholar 

  26. Gupta V, Singh A, Sharma K, Mittal H (2018) A novel differential evolution test case optimisation (detco) technique for branch coverage fault detection. In: Smart computing and informatics. Springer, pp. 245–254

  27. Pandey AC, Rajpoot DS, Saraswat M (2016) Data clustering using hybrid improved cuckoo search method. In: 2016 Ninth international conference on contemporary computing (IC3). IEEE, pp. 1–6

  28. Rarità L, Stamova I, Tomasiello S (2021) Numerical schemes and genetic algorithms for the optimal control of a continuous model of supply chains. Appl Math Comput 388:125464

    MathSciNet  MATH  Google Scholar 

  29. D’Apice C, Manzo R, Rarità L (2011) Splitting of traffic flows to control congestion in special events. Int J Math Math Sci. https://doi.org/10.1155/2011/563171

    Article  MathSciNet  MATH  Google Scholar 

  30. Göttlich S, Herty M, Ringhofer C (2010) Optimization of order policies in supply networks. Eur J Oper Res 202(2):456–465

    Article  MathSciNet  MATH  Google Scholar 

  31. Mittal H, Saraswat M (2019) An automatic nuclei segmentation method using intelligent gravitational search algorithm based superpixel clustering. Swarm Evol Comput 45:15–32

    Article  Google Scholar 

  32. Pal R, Pandey HMA, Saraswat M (2016) Beecp: biogeography optimization-based energy efficient clustering protocol for hwsns. In: 2016 ninth international conference on contemporary computing (IC3). IEEE, pp. 1–6

  33. Tripathi AK, Sharma K, Bala M (2018) A novel clustering method using enhanced grey wolf optimizer and mapreduce. Big Data Res 14:93–100

    Article  Google Scholar 

  34. Pourakbar M, Farahani RZ, Asgari N (2007) A joint economic lot-size model for an integrated supply network using genetic algorithm. Appl Math Comput 189(1):583–596

    MathSciNet  MATH  Google Scholar 

  35. Gupta M, Parmar G, Gupta R, Saraswat M (2015) Discrete wavelet transform-based color image watermarking using uncorrelated color space and artificial bee colony. Int J Comput Intell Syst 8(2):364–380

    Article  Google Scholar 

  36. Bansal JC, Singh P, Saraswat M, Verma A, Jadon SS, Abraham A (2011) Inertia weight strategies in particle swarm optimization. In: Third world congress on nature and biologically inspired computing. IEEE 2011:633–640

  37. Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47

    Article  Google Scholar 

  38. Łukasik S, Kowalski PA, Charytanowicz M, Kulczycki P (2017) Data clustering with grasshopper optimization algorithm. In: 2017 Federated conference on computer science and information systems (FedCSIS). IEEE, pp. 71–74

  39. Barman M, Choudhury ND, Sutradhar S (2018) A regional hybrid goa-svm model based on similar day approach for short-term load forecasting in Assam, India. Energy 145:710–720

    Article  Google Scholar 

  40. Ibrahim HT, Mazher WJ, Ucan ON, Bayat O (2019) A grasshopper optimizer approach for feature selection and optimizing svm parameters utilizing real biomedical data sets. Neural Comput Appl 31(10):5965–5974

    Article  Google Scholar 

  41. Abualigah L, Diabat A (2020) A comprehensive survey of the grasshopper optimization algorithm: results, variants, and applications. Neural Comput Appl. https://doi.org/10.1007/s00521-020-04789-8

    Article  Google Scholar 

  42. Yang X-S, Deb S (2014) Cuckoo search: recent advances and applications. Neural Comput Appl 24(1):169–174

    Article  Google Scholar 

  43. Nadimi-Shahraki MH, Taghian S, Mirjalili S (2021) An improved grey wolf optimizer for solving engineering problems. Expert Syst Appl 166:113917

    Article  Google Scholar 

  44. Pandey AC, Rajpoot DS, Saraswat M (2020) Feature selection method based on hybrid data transformation and binary binomial cuckoo search. J Ambient Intell Humaniz Comput 11(2):719–738

    Article  Google Scholar 

  45. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  46. Guo W, Liu T, Dai F, Xu P (2020) An improved whale optimization algorithm for forecasting water resources demand. Appl Soft Comput 86:105925

    Article  Google Scholar 

  47. Chourasia S, Sharma H, Singh M, Bansal JC (2019) Global and local neighborhood based particle swarm optimization. In: Harmony search and nature inspired optimization algorithms. Springer, pp. 449–460

  48. Zhang Q, Liu W, Meng X, Yang B, Vasilakos AV (2017) Vector coevolving particle swarm optimization algorithm. Inf Sci 394:273–298

    Article  Google Scholar 

  49. Pandey AC, Tikkiwal VA (2021) Stance detection using improved whale optimization algorithm. Complex Intell Syst 7(3):1649–1672

    Article  Google Scholar 

  50. Harper FM, Konstan JA (2015) The movielens datasets: history and context. Acm Trans Interact Intell Syst 5(4):1–19

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Netaji Subhas Institute of Technology, Delhi, India

    Viomesh Kumar Singh

  2. Netaji Subhas University of Technology, Delhi, India

    Sangeeta Sabharwal

  3. Vivekananda Institute of Professional Studies, Technical Campus, College of Engineering, Delhi, India

    Goldie Gabrani

Authors
  1. Viomesh Kumar Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sangeeta Sabharwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Goldie Gabrani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Viomesh Kumar Singh.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, V.K., Sabharwal, S. & Gabrani, G. A novel collaborative filtering based recommendation system using exponential grasshopper algorithm. Evol. Intel. 16, 621–631 (2023). https://doi.org/10.1007/s12065-021-00687-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-021-00687-7

Keywords

Search

Navigation