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Presentation of a recommender system with ensemble learning and graph embedding: a case on MovieLens

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Abstract

Information technology has spread widely, and extraordinarily large amounts of data have been made accessible to users, which has made it challenging to select data that are in accordance with user needs. For the resolution of the above issue, recommender systems have emerged, which much help users go through the process of decision-making and selecting relevant data. A recommender system predicts users’ behavior to be capable of detecting their interests and needs, and it often uses the classification technique for this purpose. It may not be sufficiently accurate to employ single classification, where not all cases can be examined, which makes the method inappropriate to specific problems. In this research, group classification and the ensemble learning technique were used for increasing prediction accuracy in recommender systems. Another issue that is raised here concerns user analysis. Given the large size of the data and a large number of users, the process of user needs analysis and prediction (using a graph in most cases, representing the relations between users and their selected items) is complicated and cumbersome in recommender systems. Graph embedding was also proposed for resolution of this issue, where all or part of user behavior can be simulated through the generation of several vectors, resolving the problem of user behavior analysis to a large extent while maintaining high efficiency. In this research, individuals most similar to the target user were classified using ensemble learning, fuzzy rules, and the decision tree, and relevant recommendations were then made to each user with a heterogeneous knowledge graph and embedding vectors. This study was performed on the MovieLens datasets, and the obtained results indicated the high efficiency of the presented method.

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Notes

  1. Adaptive Neuro-Fuzzy Inference Systems

  2. Support Vector Regression

  3. Root of the Mean Square Error

References

  1. Bai J, Li L, Zeng D (2019) HiWalk: learning node embeddings from heterogeneous networks. Inf Syst 81:82–91

    Article  Google Scholar 

  2. Barbin JP, Yousefi S, Masoumi B (2020) Efficient service recommendation using ensemble learning in the internet of things (IoT). J Ambient Intell Humaniz Comput 11(3):1339–1350

  3. Barkan O, Koenigstein N (2016) Item2vec: neural item embedding for collaborative filtering. In: 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, Vietri sul Mare, pp 1–6

  4. Basile P, Greco C, Suglia A, Semeraro G (2019) Bridging the gap between linked open data-based recommender systems and distributed representations. Inf Syst 86:1–8

    Article  Google Scholar 

  5. Ben-Lhachemi N (2018) Using tweets embeddings for hashtag recommendation in twitter. Procedia Comput Sci 127:7–15

    Article  Google Scholar 

  6. Berahmand K, Bouyer A, Samadi N (2018) A new centrality measure based on the negative and positive effects of clustering coefficient for identifying influential spreaders in complex networks. Chaos, Solitons Fractals 110:41–54

    Article  Google Scholar 

  7. Berahmand K, Bouyer A, Samadi N (2019) A new local and multidimensional ranking measure to detect spreaders in social networks. Computing 101(11):1711–1733

    Article  MathSciNet  Google Scholar 

  8. Boongoen T, Iam-On N (2018) Cluster ensembles: a survey of approaches with recent extensions and applications. Comput Sci Rev 28:1–25

    Article  MathSciNet  Google Scholar 

  9. Borràs J, Moreno A, Valls A (2014) Intelligent tourism recommender systems: a survey. Expert Syst Appl 41(16):7370–7389

    Article  Google Scholar 

  10. Cai H, Zheng VW, Chang KC-C (2018) A comprehensive survey of graph embedding: problems, techniques, and applications. IEEE Trans Knowl Data Eng 30(9):1616–1637

    Article  Google Scholar 

  11. Cao S, Lu W, Xu Q (2015) GraRep: learning graph representations with global structural information. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp 891–900

  12. Chang S, Han W, Tang J, Qi GJ, Aggarwal CC, Huang TS (2015) Heterogeneous network embedding via deep architectures. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 119–128

  13. da Costa Fortes A, Manzato MG (2014) Ensemble learning in recommender systems: Combining multiple user interactions for ranking personalization. In: Proceedings of the 20th Brazilian Symposium on Multimedia and the Web, pp 47–54

  14. Dietterich TG (2000) Ensemble methods in machine learning. In: International Workshop on Multiple Classifier Systems. Springer, Berlin, Heidelberg, pp 1–15

  15. Forouzandeh S, Aghdam AR (2019) Health recommender system in social networks: a case of facebook. Webology 16(1):1–16

  16. Forouzandeh S, Aghdam AR, Forouzandeh S, Xu S (2020) Addressing the cold-start problem using data mining techniques and improving recommender systems by cuckoo algorithm: a case study of Facebook. Comput Sci Eng 22(4):62–73

    Article  Google Scholar 

  17. Forouzandeh S, Soltanpanah H, Sheikhahmadi A (2014) Content marketing through data mining on Facebook social network. Webology 11(1):1–11

  18. Forouzandeh S, Soltanpanah H, Sheikhahmadi A (2015) Application of data mining in designing a recommender system on social networks. Int J Comput Appl 124(1):1–7

    Google Scholar 

  19. Forouzandeh S et al (2017) Recommender system for users of internet of things (IOT). IJCSNS 17(8):46

    Google Scholar 

  20. Forouzandeh S, Sheikhahmadi A, Aghdam AR, Xu S (2018) New centrality measure for nodes based on user social status and behavior on Facebook. Int J Web Inf Syst 14(2):158–176

  21. Golzardi E, Sheikhahmadi A, Abdollahpouri A (2019) Detection of trust links on social networks using dynamic features. Physica A 527:121269

    Article  Google Scholar 

  22. Grbovic M, Radosavljevic V, Djuric N, Bhamidipati N, Savla J, Bhagwan V, Sharp D (2015) E-commerce in your inbox: Product recommendations at scale. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 1809–1818

  23. Grover A, Leskovec J (2016) node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 855–864

  24. Guàrdia-Sebaoun E, Guigue V, Gallinari P (2015) Latent trajectory modeling: a light and efficient way to introduce time in recommender systems. In: Proceedings of the 9th ACM Conference on Recommender Systems, pp 281–284

  25. Hamilton WL, Ying R, Leskovec J (2017) Representation learning on graphs: Methods and applications. arXiv preprint arXiv:1709.05584

  26. Ho TK, Hull JJ, Srihari SN (1994) Decision combination in multiple classifier systems. IEEE Trans Pattern Anal Mach Intell 16(1):66–75

    Article  Google Scholar 

  27. Islam MZ, Liu J, Liu L, Li J, Kang W (2019) Semantic explanations in ensemble learning. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Cham, pp 29–41

  28. Jahrer M, Töscher A, Legenstein R (2010) Combining predictions for accurate recommender systems. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol 25, pp 693–702

  29. Jendoubi S, Martin A, Liétard L, Ben Hadji H, Ben Yaghlane B (2017) Two evidential data based models for influence maximization in twitter. Knowl-Based Syst 121:58–70

    Article  Google Scholar 

  30. Khan Z, Iltaf N, Afzal H, Abbas H (2019) Enriching non-negative matrix factorization with contextual Embeddings for recommender systems. Neurocomputing 380:246–258

  31. Koren Y, Bell R (2015) Advances in collaborative filtering. In: Recommender systems handbook. Springer, Boston, pp 77–118

  32. Krawczyk B, Minku LL, Gama J, Stefanowski J, Woźniak M (2017) Ensemble learning for data stream analysis: a survey. Inf Fusion 37:132–156

    Article  Google Scholar 

  33. Krogh A, Vedelsby J (1995) Neural network ensembles, cross validation, and active learning. Adv Neural Inf Process Syst 7:231–238

  34. Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: International Conference on Machine Learning, vol 27, pp 1188–1196

  35. Lerato M, Esan OA, Ebunoluwa AD, Ngwira SM, Zuva T (2015) A survey of recommender system feedback techniques, comparison and evaluation metrics. In: 2015 International Conference on Computing, Communication and Security (ICCCS). IEEE, Pamplemousses, pp 1–4

  36. Lin Y, Liu Z, Sun M, Liu Y, Zhu X (2015) Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, pp 2181–2187

  37. Mikolov T et al (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781

  38. Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp 3111–3119

  39. Mohammadpour T, Bidgoli AM, Enayatifar R, Javadi HHS (2019) Efficient clustering in collaborative filtering recommender system: hybrid method based on genetic algorithm and gravitational emulation local search algorithm. Genomics 111(6):1902–1912

    Article  Google Scholar 

  40. Nie F, Zhu W, Li X (2017) Unsupervised large graph embedding. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, pp 2422–2428

  41. Nilashi M, Bagherifard K, Rahmani M, Rafe V (2017) A recommender system for tourism industry using cluster ensemble and prediction machine learning techniques. Comput Ind Eng 109:357–368

    Article  Google Scholar 

  42. Nilashi M, Ibrahim O, Bagherifard K (2018) A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Syst Appl 92:507–520

    Article  Google Scholar 

  43. Nilashi M, Ibrahim OB, Ithnin N (2014) Hybrid recommendation approaches for multi-criteria collaborative filtering. Expert Syst Appl 41(8):3879–3900

    Article  Google Scholar 

  44. Palumbo E, Monti D, Rizzo G, Troncy R, Baralis E (2020) entity2rec: Property-specific knowledge graph embeddings for item recommendation. Expert Syst Appl 151:113235

    Article  Google Scholar 

  45. Pennington J, Socher R, Manning CD (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 1532–1543

  46. Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol 24, pp 701–710

  47. Porta A, Guzzetti S, Montano N, Furlan R, Pagani M, Malliani A, Cerutti S (2001) Entropy, entropy rate, and pattern classification as tools to typify complexity in short heart period variability series. IEEE Trans Biomed Eng 48(11):1282–1291

    Article  Google Scholar 

  48. Pujahari A, Sisodia DS (2019) Modeling side information in preference relation based restricted boltzmann machine for recommender systems. Inf Sci 490:126–145

    Article  Google Scholar 

  49. Qiu L, Gao S, Lyu Q, Guo J, Gallinari P (2018) A novel non-Gaussian embedding based model for recommender systems. Neurocomputing 278:144–152

    Article  Google Scholar 

  50. Ren J, Long J, Xu Z (2019) Financial news recommendation based on graph embeddings. Decis Support Syst 125:113115

    Article  Google Scholar 

  51. Ricci F, Rokach L, Shapira B (2011) Introduction to recommender systems handbook. In: Recommender systems handbook. Springer, Boston, pp 1–35

  52. Ristoski P, Mencía EL, Paulheim H (2014) A hybrid multi-strategy recommender system using linked open data. In: Semantic web evaluation challenge. Springer, Cham, pp 150–156

  53. Rostami M, Forouzandeh S, Berahmand K, Soltani M (2020) Integration of multi-objective PSO based feature selection and node centrality for medical datasets. Genomics 112(8):4370–4384

    Article  Google Scholar 

  54. Sadeghian A et al (2019) Hotel2vec: Learning Attribute-Aware Hotel Embeddings with Self-Supervision. arXiv preprint arXiv:1910.03943

  55. Seo Y-D, Kim YG, Lee E, Baik DK (2017) Personalized recommender system based on friendship strength in social network services. Expert Syst Appl 69:135–148

    Article  Google Scholar 

  56. Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) Line: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, vol 18, pp 1067–1077

  57. Valcarce D, Landin A, Parapar J, Barreiro Á (2019) Collaborative filtering embeddings for memory-based recommender systems. Eng Appl Artif Intell 85:347–356

    Article  Google Scholar 

  58. Vasile F, Smirnova E, Conneau A (2016) Meta-prod2vec: product embeddings using side-information for recommendation. In: Proceedings of the 10th ACM Conference on Recommender Systems, vol 7, pp 225–232

  59. Wang Z, Zhang J, Feng J, Chen Z (2014) Knowledge graph embedding by translating on hyperplanes. AAAI 14(2014):1112–1119

  60. Wang X, Cui P, Wang J, Pei J, Zhu W, Yang S (2017) Community preserving network embedding. AAAI 17:203–209

  61. Wang H et al (2018) Ripplenet: Propagating user preferences on the knowledge graph for recommender systems. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

    Google Scholar 

  62. Wei X, Xu L, Cao B, Yu PS (2017) Cross view link prediction by learning noise-resilient representation consensus. In: Proceedings of the 26th International Conference on World Wide Web, vol 3, pp 1611–1619

  63. Wolpert DH (2002) The supervised learning no-free-lunch theorems. In: Soft computing and industry. Springer, London, pp. 25–42

  64. Woźniak M, Graña M, Corchado E (2014) A survey of multiple classifier systems as hybrid systems. Inf Fusion 16:3–17

    Article  Google Scholar 

  65. Xie Y, Gong M, Wang S, Liu W, Yu B (2019) Sim2vec: node similarity preserving network embedding. Inf Sci 495:37–51

    Article  MathSciNet  Google Scholar 

  66. Yu L, Cui P, Song C, Zhang T, Yang S (2017) A temporally heterogeneous survival framework with application to social behavior dynamics. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol 13, pp 1295–1304

  67. Yue X, Wang Z, Huang J, Parthasarathy S, Moosavinasab S, Huang Y, Lin SM, Zhang W, Zhang P, Sun H (2020) Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4):1241–1251

    Google Scholar 

  68. Zareie A, Sheikhahmadi A, Jalili M (2019) Identification of influential users in social networks based on users’ interest. Inf Sci 493:217–231

    Article  MathSciNet  Google Scholar 

  69. Zenobi G, Cunningham P (2001) Using diversity in preparing ensembles of classifiers based on different feature subsets to minimize generalization error. In: European Conference on Machine Learning. Springer, Berlin, Heidelberg, pp 576–587

  70. Zhang F, Gong T, Lee VE, Zhao G, Rong C, Qu G (2016) Fast algorithms to evaluate collaborative filtering recommender systems. Knowl-Based Syst 96:96–103

    Article  Google Scholar 

  71. Zhang M, Wang J, Wang W (2018) HeteRank: a general similarity measure in heterogeneous information networks by integrating multi-type relationships. Inf Sci 453:389–407

    Article  MathSciNet  Google Scholar 

  72. Zhang W, Zhang X, Wang H, Chen D (2019) A deep variational matrix factorization method for recommendation on large scale sparse dataset. Neurocomputing 334:206–218

    Article  Google Scholar 

  73. Zhang W, Zou H, Luo L, Liu Q, Wu W, Xiao W (2016) Predicting potential side effects of drugs by recommender methods and ensemble learning. Neurocomputing 173:979–987

    Article  Google Scholar 

  74. Zhou H, Zhao Z, Li C, Liang Y, Zeng Q (2019) Rank2vec: learning node embeddings with local structure and global ranking. Expert Syst Appl 136:276–287

    Article  Google Scholar 

  75. Zhou C, Liu Y, Liu X, Liu Z, Gao J (2017) Scalable graph embedding for asymmetric proximity. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, pp 2942–2948

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Authors and Affiliations

  1. Department of Computer Engineering, University of Applied Science and Technologys, Center of Tehran Municipality ICT org, Tehran, Iran

    Saman Forouzandeh

  2. Department of Science and Engineering, Queensland University of Technology, Brisbane, Australia

    Kamal Berahmand

  3. Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran

    Mehrdad Rostami

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  1. Saman Forouzandeh
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  2. Kamal Berahmand
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  3. Mehrdad Rostami
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Correspondence to Saman Forouzandeh.

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Forouzandeh, S., Berahmand, K. & Rostami, M. Presentation of a recommender system with ensemble learning and graph embedding: a case on MovieLens. Multimed Tools Appl 80, 7805–7832 (2021). https://doi.org/10.1007/s11042-020-09949-5

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