[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content
Springer Nature Link
Log in

Enhancing recommendation stability of collaborative filtering recommender system through bio-inspired clustering ensemble method

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In recent years, internet technologies and its rapid growth have created a paradigm of digital services. In this new digital world, users suffer due to the information overload problem and the recommender systems are widely used as a decision support tool to address this issue. Though recommender systems are proven personalization tool available, the need for the improvement of its recommendation ability and efficiency is high. Among various recommendation generation mechanisms available, collaborative filtering-based approaches are widely utilized to produce similarity-based recommendations. To improve the recommendation generation process of collaborative filtering approaches, clustering techniques are incorporated for grouping users. Though many traditional clustering mechanisms are employed for the users clustering in the existing works, utilization of bio-inspired clustering techniques needs to be explored for the generation of optimal recommendations. This article presents a new bio-inspired clustering ensemble through aggregating swarm intelligence and fuzzy clustering models for user-based collaborative filtering. The presented recommendation approaches have been evaluated on the real-world large-scale datasets of Yelp and TripAdvisor for recommendation accuracy and stability through standard evaluation metrics. The obtained results illustrate the advantageous performance of the proposed approach over its peer works of recent times.

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
£29.99 /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 includes VAT (United Kingdom)

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
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Abreu PH, Silva DC, Almeida F, Mendes-Moreira J (2014) Improving a simulated soccer team’s performance through a memory-based collaborative filtering approach. Appl Soft Comput 23:180–193

    Article  Google Scholar 

  2. Abreu PH, Silva DC, Portela J, Mendes-Moreira J, Reis LP (2014) Using model-based collaborative filtering techniques to recommend the expected best strategy to defeat a simulated soccer opponent. Intell Data Anal 18(5):973–991

    Article  Google Scholar 

  3. 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 

  4. Adomavicius G, Zhang J (2012). On the stability of recommendation algorithms. In: Proc. ACM conf. recommender syst

  5. Adomavicius G, Zhang J (2012) Stability of recommendation algorithms. ACM Trans Inf Syst (TOIS) 30(4):23

    Article  Google Scholar 

  6. Adomavicius G, Zhang J (2015) Improving stability of recommender systems: a meta-algorithmic approach. IEEE Trans Knowl Data Eng 27(6):1573–1587

    Article  Google Scholar 

  7. Aggarwal CC, Reddy CK (eds) (2013) Data clustering: algorithms and applications. CRC Press, Boca Raton

    Google Scholar 

  8. Ahmadyfard A, Modares H (2008). Combining PSO and k-means to enhance data clustering. In: International symposium on telecommunications, 2008. IST 2008. IEEE, pp 688–691

  9. Alam S, Dobbie G, Koh YS, Riddle P, Rehman SU (2014) Research on particle swarm optimization based clustering: a systematic review of literature and techniques. Swarm Evol Comput 17:1–13

    Article  Google Scholar 

  10. Almazro D, Shahatah G, Albdulkarim L, Kherees M, Martinez R, Nzoukou W (2010) A survey paper on recommender systems. arXiv preprint arXiv:1006.5278

  11. Al-Razgan M, Domeniconi C, Barbará D (2008) Random subspace ensembles for clustering categorical data. In: Okun O, Valentini G (eds) Supervised and unsupervised ensemble methods and their applications. Springer, Berlin, Heidelberg, pp 31–48

    Chapter  Google Scholar 

  12. Al-Shamri MYH (2014) Power coefficient as a similarity measure for memory-based collaborative recommender systems. Expert Syst Appl 41(13):5680–5688

    Article  Google Scholar 

  13. Amatriain X, Basilico J (2012) Netflix recommendations: beyond the 5 stars (part 1). Netflix Tech Blog. http://techblog.netflix.com/2012/04/netflix-recommendations-beyond-5-stars.html. Accessed 16 Nov 2018

  14. An J, Kang Q, Wang L, Wu Q (2013) Mussels wandering optimization: an ecologically inspired algorithm for global optimization. Cognit Comput 5(2):188–199

    Article  Google Scholar 

  15. Baghbani G, Eskandari F (2017) Calculating the required cash in bank branches: a Bayesian-data mining approach. Neural Comput Appl 30(9):2831–2841

    Article  Google Scholar 

  16. Bansal A, Chen T, Zhong S (2011) Privacy preserving back-propagation neural network learning over arbitrarily partitioned data. Neural Comput Appl 20(1):143–150

    Article  Google Scholar 

  17. Basu C, Hirsh H, Cohen W (1998). Recommendation as classification: using social and content-based information in recommendation. In: AAAI/IAAI (pp 714–720)

  18. Benabdeslem K, Allab K (2013) Bi-clustering continuous data with self-organizing map. Neural Comput Appl 22(7–8):1551–1562

    Article  Google Scholar 

  19. Bezdek JC, Ehrlich R, Full W (1984) FCM: the fuzzy c-means clustering algorithm. Comput Geosci 10(2–3):191–203

    Article  Google Scholar 

  20. Bilge A, Polat H (2013) A comparison of clustering-based privacy-preserving collaborative filtering schemes. Appl Soft Comput 13(5):2478–2489

    Article  Google Scholar 

  21. Birtolo C, Ronca D (2013) Advances in clustering collaborative filtering by means of fuzzy C-means and trust. Expert Syst Appl 40(17):6997–7009

    Article  Google Scholar 

  22. Birtolo C, Ronca D, Armenise R (2011) Improving accuracy of recommendation system by means of item-based fuzzy clustering collaborative filtering. In: 2011 11th international conference on intelligent systems design and applications (ISDA), pp 100–106. IEEE

  23. Bobadilla J, Serradilla F, Bernal J (2010) A new collaborative filtering metric that improves the behavior of recommender systems. Knowl Based Syst 23(6):520–528

    Article  Google Scholar 

  24. Bobadilla J, Hernando A, Ortega F, Bernal J (2011) A framework for collaborative filtering recommender systems. Expert Syst Appl 38(12):14609–14623

    Article  Google Scholar 

  25. Bobadilla J, Ortega F, Hernando A (2012) A collaborative filtering similarity measure based on singularities. Inf Process Manag 48(2):204–217

    Article  Google Scholar 

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

    Article  Google Scholar 

  27. Bouchachia A (2007) Learning with partly labeled data. Neural Comput Appl 16(3):267–293

    Article  Google Scholar 

  28. Bouguessa M, Wang S (2009) Mining projected clusters in high-dimensional spaces. IEEE Trans Knowl Data Eng 21(4):507–522

    Article  Google Scholar 

  29. Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User Adap Interact 12(4):331–370

    Article  MATH  Google Scholar 

  30. Cai W (2015) A manifold learning framework for both clustering and classification. Knowl Based Syst 89:641–653

    Article  Google Scholar 

  31. Candillier L, Meyer F, Boullé M (2007) Comparing state-of-the-art collaborative filtering systems. In: International workshop on machine learning and data mining in pattern recognition. Springer, Berlin, Heidelberg, pp 548–562

  32. Carrer-Neto W, Hernández-Alcaraz ML, Valencia-García R, García-Sánchez F (2012) Social knowledge-based recommender system. Application to the movies domain. Expert Syst Appl 39(12):10990–11000

    Article  Google Scholar 

  33. Chen X (2015) A new clustering algorithm based on near neighbor influence. Expert Syst Appl 42(21):7746–7758

    Article  Google Scholar 

  34. Chen X, Xu X, Huang JZ, Ye Y (2013) TW-k-means: automated two-level variable weighting clustering algorithm for multiview data. IEEE Trans Knowl Data Eng 25(4):932–944

    Article  Google Scholar 

  35. Chen S, Xu Z, Tang Y (2014) A hybrid clustering algorithm based on fuzzy C-means and improved particle swarm optimization. Arab J Sci Eng 39(12):8875–8887

    Article  MathSciNet  MATH  Google Scholar 

  36. Cheng LC, Wang HA (2014) A fuzzy recommender system based on the integration of subjective preferences and objective information. Appl Soft Comput 18:290–301

    Article  Google Scholar 

  37. Choi SM, Ko SK, Han YS (2012) A movie recommendation algorithm based on genre correlations. Expert Syst Appl 39(9):8079–8085

    Article  Google Scholar 

  38. Cleger-Tamayo S, Fernández-Luna JM, Huete JF (2012) Top-N news recommendations in digital newspapers. Knowl Based Syst 27:180–189

    Article  Google Scholar 

  39. de Jesús Rubio J, Pacheco J (2009) An stable online clustering fuzzy neural network for nonlinear system identification. Neural Comput Appl 18(6):633–641

    Article  Google Scholar 

  40. Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7(Jan):1–30

    MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  42. Domeniconi C, Al-Razgan M (2009) Weighted cluster ensembles: methods and analysis. ACM Trans Knowl Discov Data (TKDD) 2(4):17

    Google Scholar 

  43. Ericson K, Pallickara S (2013) On the performance of high dimensional data clustering and classification algorithms. Future Gener Comput Syst 29(4):1024–1034

    Article  Google Scholar 

  44. Fan XP, Xie YS, Liao ZF, Li XQ, Liu LM (2011). A Weighted cluster ensemble algorithm based on graph. In: 2011 IEEE 10th international conference on trust, security and privacy in computing and communications (TrustCom). IEEE, pp 1519–1523

  45. Ferreira LN, Zhao L (2016) Time series clustering via community detection in networks. Inf Sci 326:227–242

    Article  MathSciNet  MATH  Google Scholar 

  46. Fred AL, Jain AK (2002) Data clustering using evidence accumulation. In: Proceedings of 16th international conference on pattern recognition, 2002. IEEE, vol 4, pp 276–280

  47. Gabrielsson S, Gabrielsson S (2006) The use of self-organizing maps in recommender systems. Master’s Thesis, Computer Science, Uppsala University

  48. Goldberg D, Nichols D, Oki BM, Terry D (1992) Using collaborative filtering to weave an information tapestry. Commun ACM 35(12):61–70

    Article  Google Scholar 

  49. Gupta A, Tripathy BK (2014) A generic hybrid recommender system based on neural networks. In: 2014 IEEE international advance computing conference (IACC). IEEE, pp 1248–1252

  50. Gutierrez-Rodríguez AE, Martínez-Trinidad JF, García-Borroto M, Carrasco-Ochoa JA (2015) Mining patterns for clustering on numerical datasets using unsupervised decision trees. Knowl Based Syst 82:70–79

    Article  Google Scholar 

  51. 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 

  52. Huang D, Lai JH, Wang CD (2015) Combining multiple clusterings via crowd agreement estimation and multi-granularity link analysis. Neurocomputing 170:240–250

    Article  Google Scholar 

  53. Huang S, Wang H, Li D, Yang Y, Li T (2015) Spectral co-clustering ensemble. Knowl Based Syst 84:46–55

    Article  Google Scholar 

  54. Iam-On N, Boongeon T, Garrett S, Price C (2012) A link-based cluster ensemble approach for categorical data clustering. IEEE Trans Knowl Data Eng 24(3):413–425

    Article  Google Scholar 

  55. Izakian H, Abraham A (2011) Fuzzy C-means and fuzzy swarm for fuzzy clustering problem. Expert Syst Appl 38(3):1835–1838

    Article  Google Scholar 

  56. Jenssen R (2013) Mean vector component analysis for visualization and clustering of nonnegative data. IEEE Trans Neural Netw Learn Syst 24(10):1553–1564

    Article  Google Scholar 

  57. Jeong B, Lee J, Cho H (2009) User credit-based collaborative filtering. Expert Syst Appl 36(3):7309–7312

    Article  Google Scholar 

  58. Jia J, Xiao X, Liu B (2012) Similarity-based spectral clustering ensemble selection. In: 2012 9th international conference on fuzzy systems and knowledge discovery (FSKD). IEEE, pp 1071–1074

  59. Jing L, Tian K, Huang JZ (2015) Stratified feature sampling method for ensemble clustering of high dimensional data. Pattern Recognit 48(11):3688–3702

    Article  Google Scholar 

  60. Kang Q, Liu S, Zhou M, Li S (2016) A weight-incorporated similarity-based clustering ensemble method based on swarm intelligence. Knowl Based Syst 104:156–164

    Article  Google Scholar 

  61. Karypis G, Kumar V (1998) A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J Sci Comput 20(1):359–392

    Article  MathSciNet  MATH  Google Scholar 

  62. Karypis G, Aggarwal R, Kumar V, Shekhar S (1999) Multilevel hypergraph partitioning: applications in VLSI domain. IEEE Trans Very Large Scale Integr (VLSI) Syst 7(1):69–79

    Article  Google Scholar 

  63. Kim HN, El-Saddik A, Jo GS (2011) Collaborative error-reflected models for cold-start recommender systems. Decision Support Syst 51(3):519–531

    Article  Google Scholar 

  64. Kittler J, Hatef M, Duin RP, Matas J (1998) On combining classifiers. IEEE Trans Pattern Anal Mach Intell 20(3):226–239

    Article  Google Scholar 

  65. Koohi H, Kiani K (2016) User based collaborative filtering using fuzzy C-means. Measurement 91:134–139

    Article  Google Scholar 

  66. Koren Y (2009) The bellkor solution to the netflix grand prize. Netflix Prize Doc 81:1–10

    Google Scholar 

  67. Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37

    Article  Google Scholar 

  68. Landau L (2011) An introduction to recommender systems. Cambridge University Press, New York

    Google Scholar 

  69. Leino J (2014) User factors in recommender systems: case studies in e-commerce, news recommending, and e-learning

  70. Li Q, Kim BM (2003) Clustering approach for hybrid recommender system. In: Proceedings of IEEE/WIC international conference on web intelligence, 2003. WI 2003. IEEE, pp 33–38

  71. Li Q, Myaeng SH, Kim BM (2007) A probabilistic music recommender considering user opinions and audio features. Inf Process Manag 43(2):473–487

    Article  Google Scholar 

  72. Li C, Zhou J, Kou P, Xiao J (2012) A novel chaotic particle swarm optimization based fuzzy clustering algorithm. Neurocomputing 83:98–109

    Article  Google Scholar 

  73. Liu ZG, Pan Q, Dezert J, Mercier G (2015) Credal c-means clustering method based on belief functions. Knowl Based Syst 74:119–132

    Article  Google Scholar 

  74. Logesh R, Subramaniyaswamy V, Vijayakumar V, Gao XZ, Indragandhi V (2018) A hybrid quantum-induced swarm intelligence clustering for the urban trip recommendation in smart city. Future Gener Comput Syst 83:653–673

    Article  Google Scholar 

  75. Lu L, Zhang H (2015) A tree-structured representation for book author and its recommendation using multilayer SOM. In: 2015 International joint conference on neural networks (IJCNN). IEEE, pp 1–8

  76. Lü L, Medo M, Yeung CH, Zhang YC, Zhang ZK, Zhou T (2012) Recommender systems. Phys Rep 519(1):1–49

    Article  Google Scholar 

  77. Luo H, Jing F, Xie X (2006). Combining multiple clusterings using information theory based genetic algorithm. In: 2006 international conference on computational intelligence and security. IEEE, vol 1, pp 84–89

  78. Olmo JL, Romero JR, Ventura S (2014) Swarm-based metaheuristics in automatic programming: a survey. Wiley Interdiscip Rev Data Min Knowl Discov 4(6):445–469

    Article  Google Scholar 

  79. Pal NR, Pal K, Keller JM, Bezdek JC (2005) A possibilistic fuzzy c-means clustering algorithm. IEEE Trans Fuzzy Syst 13(4):517–530

    Article  Google Scholar 

  80. Park DH, Kim HK, Choi IY, Kim JK (2012) A literature review and classification of recommender systems research. Expert Syst Appl 39(11):10059–10072

    Article  Google Scholar 

  81. Pei Z, Hua X, Han J (2008) The clustering algorithm based on particle swarm optimization algorithm. In: 2008 International conference on intelligent computation technology and automation (ICICTA). IEEE, vol 1, pp 148–151

  82. Petridou SG, Koutsonikola VA, Vakali AI, Papadimitriou GI (2008) Time-aware web users’ clustering. IEEE Trans Knowledge Data Eng 20(5):653–667

    Article  Google Scholar 

  83. Pilászy I, Tikk D (2009) Recommending new movies: even a few ratings are more valuable than metadata. In: Proceedings of the third ACM conference on recommender systems. ACM, pp 93–100

  84. Qin C, Song S, Huang G, Zhu L (2015) Unsupervised neighborhood component analysis for clustering. Neurocomputing 168:609–617

    Article  Google Scholar 

  85. Ramezani M, Moradi P, Akhlaghian F (2014) A pattern mining approach to enhance the accuracy of collaborative filtering in sparse data domains. Phys A Stat Mech Appl 408:72–84

    Article  Google Scholar 

  86. Rana C, Jain SK (2014) An extended evolutionary clustering algorithm for an adaptive recommender system. Soc Netw Anal Min 4(1):164

    Article  Google Scholar 

  87. Rana S, Jasola S, Kumar R (2013) A boundary restricted adaptive particle swarm optimization for data clustering. Int J Mach Learn Cybern 4(4):391–400

    Article  Google Scholar 

  88. Ravi L, Vairavasundaram S (2016) A collaborative location based travel recommendation system through enhanced rating prediction for the group of users. Comput Intell Neurosci 2016:7

    Article  Google Scholar 

  89. Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J (1994) GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM conference on computer supported cooperative work. ACM, pp 175–186

  90. Saha I, Sarkar JP, Maulik U (2015) Ensemble based rough fuzzy clustering for categorical data. Knowl Based Syst 77:114–127

    Article  Google Scholar 

  91. Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web. ACM, pp 285–295

  92. Sarwar BM, Karypis G, Konstan J, Riedl J (2002) Recommender systems for large-scale e-commerce: scalable neighborhood formation using clustering. In: Proceedings of the fifth international conference on computer and information technology, vol 1, pp 291–324

  93. Schroder G, Thiele M, Lehner W (2011) Setting goals and choosing metrics for recommender system evaluation. In: Proceedings of the workshop on human decision making in recommender systems and user-centric evaluation of recommender systems and their interfaces. CEUR workshop proceedings, vol 811, pp 78–85)

  94. Shani G, Gunawardana A (2011) Evaluating recommendation systems. In: Ricci F, Rokach L, Shapira B, Kantor P (eds) Recommender systems handbook. Springer, Boston, pp 257–297

    Chapter  Google Scholar 

  95. Shao L, Zhang J, Wei Y, Zhao J, Xie B, Mei H (2007) Personalized QoS prediction for web services via collaborative filtering. In: IEEE international conference on web services, 2007. ICWS 2007. IEEE, pp 439–446

  96. Silva Filho TM, Pimentel BA, Souza RM, Oliveira AL (2015) Hybrid methods for fuzzy clustering based on fuzzy c-means and improved particle swarm optimization. Expert Syst Appl 42(17–18):6315–6328

    Article  Google Scholar 

  97. Strehl A, Ghosh J (2002) Cluster ensembles—a knowledge reuse framework for combining multiple partitions. J Mach Learn Res 3(Dec):583–617

    MathSciNet  MATH  Google Scholar 

  98. Sun YF, Liang YC, Zhang WL, Lee HP, Lin WZ, Cao LJ (2005) Optimal partition algorithm of the RBF neural network and its application to financial time series forecasting. Neural Comput Appl 14(1):36–44

    Article  Google Scholar 

  99. Topchy AP, Law MH, Jain AK, Fred AL (2004) Analysis of consensus partition in cluster ensemble. In: Fourth IEEE international conference on data mining, 2004. ICDM’04. IEEE, pp 225–232

  100. Tsai CF, Hung C (2012) Cluster ensembles in collaborative filtering recommendation. Appl Soft Comput 12(4):1417–1425

    Article  Google Scholar 

  101. Vairavasundaram S, Varadharajan V, Vairavasundaram I, Ravi L (2015) Data mining-based tag recommendation system: an overview. Wiley Interdiscip Rev Data Min Knowl Discov 5(3):87–112

    Article  Google Scholar 

  102. Van der Merwe DW, Engelbrecht AP (2003) Data clustering using particle swarm optimization. In: The 2003 congress on evolutionary computation, 2003. CEC’03. IEEE, vol 1, pp 215–220

  103. Wang L, Rege M, Dong M, Ding Y (2012) Low-rank kernel matrix factorization for large-scale evolutionary clustering. IEEE Trans Knowl Data Eng 24(6):1036–1050

    Article  Google Scholar 

  104. Wang Z, Yu X, Feng N, Wang Z (2014) An improved collaborative movie recommendation system using computational intelligence. J Vis Lang Comput 25(6):667–675

    Article  Google Scholar 

  105. Wen H, Ding G, Liu C, Wang J (2014) Matrix factorization meets cosine similarity: addressing sparsity problem in collaborative filtering recommender system. In: Asia-Pacific web conference. Springer, Cham, pp 306–317

  106. Wilkin GA, Huang X (2007) K-means clustering algorithms: implementation and comparison. In: Second international multi-symposiums on computer and computational sciences, 2007. IMSCCS 2007. IEEE, pp 133–136

  107. Xu R, Wunsch D (2005) Survey of clustering algorithms. IEEE Trans Neural Netw 16(3):645–678

    Article  Google Scholar 

  108. Xu M, Wang G, Chung FL, Wang S (2016) Linear combination of densities and its direct estimation framework with applications. Neural Comput Appl 27(6):1477–1495

    Article  Google Scholar 

  109. Yeh YC, Wang WJ, Chiou CW (2010) A novel fuzzy c-means method for classifying heartbeat cases from ECG signals. Measurement 43(10):1542–1555

    Article  Google Scholar 

  110. Zenebe A, Norcio AF (2009) Representation, similarity measures and aggregation methods using fuzzy sets for content-based recommender systems. Fuzzy Sets Syst 160(1):76–94

    Article  MathSciNet  MATH  Google Scholar 

  111. Zhang Y, Xiong X, Zhang Q (2013) An improved self-adaptive PSO algorithm with detection function for multimodal function optimization problems. Math Probl Eng 2013:716952. https://doi.org/10.1155/2013/716952

    Article  MathSciNet  MATH  Google Scholar 

  112. Zhang L, Pedrycz W, Lu W, Liu X, Zhang L (2014) An interval weighed fuzzy c-means clustering by genetically guided alternating optimization. Expert Syst Appl 41(13):5960–5971

    Article  Google Scholar 

  113. Zhang Z, Pati D, Srivastava A (2015) Bayesian clustering of shapes of curves. J Stat Plan Inference 166:171–186

    Article  MathSciNet  MATH  Google Scholar 

  114. Zhang H, Chow TW, Wu QJ (2016) Organizing books and authors by multilayer SOM. IEEE Trans Neural Netw Learn Syst 27(12):2537–2550

    Article  Google Scholar 

  115. Zhang H, Wang S, Xu X, Chow TW, Wu QJ (2018) Tree2Vector: learning a vectorial representation for tree-structured data. IEEE Trans Neural Netw Learn Syst 29(11):5304–5318

    Article  MathSciNet  Google Scholar 

  116. Zhou ZH (2012) Ensemble methods: foundations and algorithms. CRC Press, Boca Raton

    Book  Google Scholar 

Download references

Acknowledgements

The authors are grateful to Science and Engineering Research Board (SERB), Department of Science and Technology, New Delhi, for the financial support (No. YSS/2014/000718/ES). Authors express their gratitude to SASTRA Deemed University, Thanjavur, for providing the infrastructural facilities to carry out this research work.

Author information

Authors and Affiliations

  1. School of Computing, SASTRA Deemed University, Thanjavur, Tamilnadu, India

    R. Logesh, V. Subramaniyaswamy, D. Malathi & N. Sivaramakrishnan

  2. School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, Tamilnadu, India

    V. Vijayakumar

Authors
  1. R. Logesh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. Subramaniyaswamy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. D. Malathi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. N. Sivaramakrishnan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. V. Vijayakumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to V. Subramaniyaswamy.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this article.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Logesh, R., Subramaniyaswamy, V., Malathi, D. et al. Enhancing recommendation stability of collaborative filtering recommender system through bio-inspired clustering ensemble method. Neural Comput & Applic 32, 2141–2164 (2020). https://doi.org/10.1007/s00521-018-3891-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-018-3891-5

Keywords

Search

Navigation