Mobile Networks and Applications

, Volume 24, Issue 6, pp 1872–1882 | Cite as

Efficient Machine Learning Model for Movie Recommender Systems Using Multi-Cloud Environment

  • K. IndiraEmail author
  • M. K. Kavithadevi


A recommender system or a recommendation system is a subclass of information filtering system which in turn predicts the “preference” or “ratings” which a user would provide to the specified item. Recommender systems are utilized in a variety of areas comprising news, music, movies, books, search queries, social tags, research articles, and products in general. The primary aim of the recommender system is to allow the computers learn automatically without any human intervention or assistance and regulate activities consequently. The existing methods had a lower amount of search result quality and a minimum rate of ranking accuracy. To overcome this issue and to enhance the ranking quality and search result quality a novel recommender system in the multi-cloud with the use of proposed machine learning algorithm. In this proposed work (NPCA-HAC), the social data set are pre-processed to remove the noise and making them pure. Then, the method of feature selection is carried out with the use of principle component analysis method (PCA). The selected features are then clustered with the use of k-means followed by the Hierarchical Agglomerative Clustering algorithm (HAC). These clusters are then ranked by the use of trust ranking algorithm. Finally, the ranked output was evaluated and the performance measure was analyzed which provides the efficient results from the recommender system.


Recommender system Principle component analysis Hierarchical agglomerative clustering K-means Trust ranking Multi-cloud environment 



  1. 1.
    Almutairi FM, Sidiropoulos ND, Karypis G (2017) Context-aware recommendation-based learning analytics using tensor and coupled matrix factorization. IEEE J Select Topics Signal Process 11:729–741CrossRefGoogle Scholar
  2. 2.
    Thanapalasingam T, Osborne F, Birukou A, Motta E (2018) The smart book recommender: an ontology-driven application for recommending editorial productsGoogle Scholar
  3. 3.
    Chen J, Wang C, Wang J, Philip SY (2016) Recommendation for repeat consumption from user implicit feedback. IEEE Trans Knowl Data Eng 28:3083–3097CrossRefGoogle Scholar
  4. 4.
    Desai P, Telis N, Lehmann B, Bettinger K, Pritchard JK, Datta S (2018) SciReader*: a cloud-based recommender system for biomedical literature. bioRxiv: 333922Google Scholar
  5. 5.
    Wang D, Liang Y, Xu D, Feng X, Guan R (2018) A content-based recommender system for computer science publications. Knowl-Based Syst 157:1–9CrossRefGoogle Scholar
  6. 6.
    Katarya R, Verma OP (2017) An effective collaborative movie recommender system with cuckoo search. Egypt Inform J 18:105–112CrossRefGoogle Scholar
  7. 7.
    Wasid M, Ali R (2018) An improved recommender system based on multi-criteria clustering approach. Proc Comput Sci 131:93–101CrossRefGoogle Scholar
  8. 8.
    Sulikowski P, Zdziebko T, Turzyński D, Kańtoch E (2018) Human-website interaction monitoring in recommender systems. Proc Comput Sci 126:1587–1596CrossRefGoogle Scholar
  9. 9.
    Sahoo N, Krishnan R, Duncan G, Callan J (2008) On multi-component rating and collaborative filtering for recommender systems: the case of yahoo! Movies. Inf Syst ResGoogle Scholar
  10. 10.
    Wang Y, Liu J, Dong X, Liu T, Huang Y (2012) Personalized paper recommendation based on user historical behavior. Natural Language Processing and Chinese Computing, ed: Springer: 1–12Google Scholar
  11. 11.
    Gupta PP, Chavan SM (2017) A privacy-preserving QoS prediction framework for web service recommendationGoogle Scholar
  12. 12.
    Liu L, Wu L (2005) User modeling for personalized recommender systems. Tsinghua Sci Technol 10:772–777Google Scholar
  13. 13.
    Qian F, Zhang Y, Zhang Y, Duan Z (2013) Community-based user domain model collaborative recommendation algorithm. Tsinghua Sci Technol 18:353–359CrossRefGoogle Scholar
  14. 14.
    Wang M, Shi L, Liu L, Ahmed M, Panneerselvan J (2018) Hybrid recommendation–based quality of service prediction for sensor services. Int J Distrib Sensor Netw 14:1550147718774012Google Scholar
  15. 15.
    Salehi M, Kamalabadi IN, Ghoushchi MBG (2013) An effective recommendation framework for personal learning environments using a learner preference tree and a GA. IEEE Trans Learn Technol 6:350–363CrossRefGoogle Scholar
  16. 16.
    Wang S, Zheng Z, Wu Z, Lyu MR, Yang F (2015) Reputation measurement and malicious feedback rating prevention in web service recommendation systems. IEEE Trans Serv Comput 8:755–767CrossRefGoogle Scholar
  17. 17.
    Jiang M, Song D, Liao L, Zhu F (2015) A Bayesian recommender model for user rating and review profiling. Tsinghua Sci Technol 20:634–643CrossRefGoogle Scholar
  18. 18.
    de Oliveira Werneck R, de Almeida WR, Stein BV, Pazinato DV, Júnior PRM, Penatti OAB et al (2018) Kuaa: a unified framework for design, deployment, execution, and recommendation of machine learning experiments. Futur Gener Comput Syst 78:59–76CrossRefGoogle Scholar
  19. 19.
    Singh S, Sidhu J (2017) Compliance-based multi-dimensional trust evaluation system for determining trustworthiness of cloud service providers. Futur Gener Comput Syst 67:109–132CrossRefGoogle Scholar
  20. 20.
    Ding S, Xia C, Wang C, Wu D, Zhang Y (2017) Multi-objective optimization based ranking prediction for cloud service recommendation. Decis Support Syst 101:106–114CrossRefGoogle Scholar
  21. 21.
    Colombo-Mendoza LO, Valencia-García R, Colomo-Palacios R, Alor-Hernández G (2018) A knowledge-based multi-criteria collaborative filtering approach for discovering services in mobile cloud computing platforms. J Intell Inf Syst:1–25Google Scholar
  22. 22.
    Labba C, Saoud NBB, Dugdale J (2018) A predictive approach for the efficient distribution of agent-based systems on a hybrid-cloud. Futur Gener Comput Syst 86:750–764CrossRefGoogle Scholar
  23. 23.
    Inan E, Tekbacak F, Ozturk C (2018) Moreopt: a goal programming based movie recommender system. J Comput Sci 28:43–50CrossRefGoogle Scholar
  24. 24.
    Irfan R, Khalid O, Khan MUS, Chira C, Ranjan R, Zhang F et al (2017) MobiContext: a context-aware cloud-based venue recommendation framework. IEEE Trans Cloud Comput 5:712–724CrossRefGoogle Scholar
  25. 25.
    Qi L, Zhang X, Dou W, Ni Q (2017) A distributed locality-sensitive hashing-based approach for cloud service recommendation from multi-source data. IEEE J Select Areas Commun 35:2616–2624CrossRefGoogle Scholar
  26. 26.
    Osadchiy T, Poliakov I, Olivier P, Rowland M, Foster E (2019) Recommender system based on pairwise association rules. Expert Syst Appl 115:535–542CrossRefGoogle Scholar
  27. 27.
    Guo Z, Tang C, Niu W, Fu Y, Wu T, Xia H et al (2017) Fine-grained recommendation mechanism to curb astroturfing in crowdsourcing systems. IEEE Access 5:15529–15541CrossRefGoogle Scholar
  28. 28.
    Soltani S, Martin P, Elgazzar K (2018) A hybrid approach to automatic IaaS service selection. J Cloud Comput 7:12CrossRefGoogle Scholar
  29. 29.
    Orciuoli F, Parente M (2017) An ontology-driven context-aware recommender system for indoor shopping based on cellular automata. J Ambient Intell Humaniz Comput 8:937–955CrossRefGoogle Scholar
  30. 30.
    Elkahky AM, Song Y, He X (2015) A multi-view deep learning approach for cross domain user modeling in recommendation systems. Proceedings of the 24th international conference on world wide web: 278–288Google Scholar
  31. 31.
    Yang L, Bagdasaryan E, Gruenstein J, Hsieh C-K, Estrin D (2018) OpenRec: a modular framework for extensible and adaptable recommendation algorithms. Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining: 664–672Google Scholar
  32. 32.
    Zhang Y-w, Zhou Y-y, Wang F-t, Sun Z, He Q (2018) Service recommendation based on quotient space granularity analysis and covering algorithm on spark. Knowl-Based Syst 147:25–35CrossRefGoogle Scholar
  33. 33.
    Zheng M, Min F, Zhang H-R, Chen W-B (2016) Fast recommendations with the m-distance. IEEE Access 4:1464–1468CrossRefGoogle Scholar
  34. 34.
    Chavan PU, Kulkarni R Survey on CommTrust: multi-dimensional trust using mining E-commerce feedback commentsGoogle Scholar
  35. 35.
    Balaji P, Nagaraju O, Haritha D (2017) CommTrust: reputation based trust evaluation in E-commerce applications. 2017 international conference on big data analytics and computational intelligence (ICBDAC): 318–323Google Scholar
  36. 36.
    Shakeel PM, Baskar S, Dhulipala VS, Jaber MM (2018) Cloud based framework for diagnosis of diabetes mellitus using K-means clustering. Health Inform Sci Syst 6:16CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Information TechnologyThiagarajar College of EngineeringMaduraiIndia
  2. 2.Department of Computer Science and EngineeringThiagarajar College of EngineeringMaduraiIndia

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