Document Type : Original/Review Paper

Authors

1 Computer Engineering Department, Shomal University, Amol, Iran.

2 Computer Engineering Department, Shomal University, Amol, Iran

10.22044/jadm.2022.11248.2279

Abstract

Nowadays, with the expansion of the internet and its associated technologies, recommender systems have become increasingly common. In this work, the main purpose is to apply new deep learning-based clustering methods to overcome the data sparsity problem and increment the efficiency of recommender systems based on precision, accuracy, F-measure, and recall. Within the suggested model of this research, the hidden biases and input weights values of the extreme learning machine algorithm are produced by the Restricted Boltzmann Machine and then clustering is performed. Also, this study employs the ELM for two approaches, clustering of training data and determine the clusters of test data. The results of the proposed method evaluated in two prediction methods by employing average and Pearson Correlation Coefficient in the MovieLens dataset. Considering the outcomes, it can be clearly said that the suggested method can overcome the problem of data sparsity and achieve higher performance in recommender systems. The results of evaluation of the proposed approach indicate a higher rate of all evaluation metrics while using the average method results in rates of precision, accuracy, recall, and F-Measure come to 80.49, 83.20, 67.84 and 73.62 respectively.

Keywords

[1] A. Vespignani, “Predicting the behavior of techno-social systems,” Science, Vol. 325, pp. 425-428, 2009.
 
[2] D. K. Behera, M. Das, and S. Swetanisha, “Predicting users’ preferences for movie recommender system using restricted Boltzmann machine,” in Advances in Intelligent Systems and Computing, vol. 711, 2019.
 
[3] D. K. Behera, M. Das, S. Swetanisha, and B. Naik, “Collaborative filtering using restricted boltzmann machine and fuzzy C-means,” in Advances in Intelligent Systems and Computing, vol. 710, 2018.
 
[4] A. Salah, N. Rogovschi, and M. Nadif, “A dynamic collaborative filtering system via a weighted clustering approach,” Neurocomputing, vol.  175, pp. 206-215, 2015.
 
[5] L. Lü, M. Medo, C. H. Yeung, Y. C. Zhang, Z. K. Zhang, and T. Zhou, “Recommender systems,” Physics Reports, vol. 519, pp. 1-49, 2012.
 
[6] M. Salehi, I. Nakhai Kamalabadi and M. B. Ghaznavi Ghoushchi, "An effective recommendation framework for personal learning environments using a learner preference tree and a GA," in IEEE Transactions on Learning Technologies, vol. 6, no. 4, pp. 350-363, 2013.
 
[7] E. Q. Da Silva, C. G. Camilo-Junior, L. M. L. Pascoal, and T. C. Rosa, “An evolutionary approach for combining results of recommender systems techniques based on collaborative filtering,” Expert Systems with Applications, vol. 53, pp. 204-218, 2016.
 
[8] D. Jannach, M. Zanker, A. Felfernig, and G. Friedrich, Recommender systems: An introduction, Cambridge University Press, Cambridge, 2010.
 
[9] S. Wei, X. Zheng, D. Chen, and C. Chen, “A hybrid approach for movie recommendation via tags and ratings,” Electronic Commerce Research and Applications, vol. 18, pp. 83-94, 2016.
 
[10] D. Almazro, G. Shahatah, L. Albdulkarim, M. Kherees, R. Martinez, and W. Nzoukou, “A Survey Paper on Recommender Systems”, vol. 11, 2010.
 
[11] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Item-based collaborative filtering recommendation algorithms,” in Proceedings of the 10th International Conference on World Wide Web, WWW 2001, pp. 285-295, 2001.
 
[12] H. Koohi and K. Kiani, “User based Collaborative Filtering using fuzzy C-means,” Measurement: Journal of the International Measurement Confederation, vol. 91, pp. 134-139, 2016.
 
[13] R. Salakhutdinov, A. Mnih, and G. Hinton, “Restricted Boltzmann machines for collaborative filtering,” in ACM International Conference Proceeding Series, pp. 791–798, 2007.
 
[14] D. Goldberg, D. Nichols, B. M. Oki, and D. Terry, “Using collaborative filtering to Weave an Information tapestry,” Communications of the ACM, vol. 35, pp. 61–70, 1992.
 
[15] K. Ericson and S. Pallickara, “On the performance of high dimensional data clustering and classification algorithms,” Future Generation Computer Systems, vol. 29, pp. 1024-1034, 2013.
 
[16] J. Bobadilla, F. Ortega, A. Hernando, and A. Gutiérrez, “Recommender systems survey,” Knowledge-Based Systems, vol. 46, pp. 109-132, 2013.
 
[17] C. F. Tsai and C. Hung, “Cluster ensembles in collaborative filtering recommendation,” Applied Soft Computing Journal, vol. 12, pp. 1417-1425, 2012.
 
[18] A. Jameson and B. Smyth, “Recommendation to groups,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4321, pp. 596-627, 2007.
 
[19] H. Koohi and K. Kiani, “A new method to find neighbor users that improves the performance of Collaborative Filtering,” Expert Systems with Applications, vol. 83, pp. 30-39, 2017.
 
[20] R. Katarya, “Movie recommender system with metaheuristic artificial bee,” Neural Computing and Applications, 2018.
 
[21] S. P. Singh and S. Solanki, “A Movie Recommender System Using Modified Cuckoo Search,” in Lecture Notes in Electrical Engineering, vol. 545, pp. 471-482, 2019.
 
[22] S. Verma, P. Patel, and A. Majumdar, “Collaborative Filtering with Label Consistent Restricted Boltzmann Machine,” in 2017 9th International Conference on Advances in Pattern Recognition, ICAPR 2017, pp. 1-6, 2018.
 
[23] Q. He, X. Jin, C. Du, F. Zhuang, and Z. Shi, “Clustering in extreme learning machine feature space,” Neurocomputing, vol. 128, pp. 88-95, 2014.
 
[24] G. E. Hinton, “A Practical Guide to Training Restricted Boltzmann Machines,”, Lecture Notes in Computer Science, vol. 7700, pp. 599-619, 2012.
 
[25] J. Kleinberg and M. Sandler, “Convergent algorithms for collaborative filtering,” in Proceedings of the ACM Conference on Electronic Commerce, pp.  1–10, 2003.
 
[26] G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions,” in IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, pp. 734-749, 2005.
 
[27] Z. D. Zhao and M. S. Shang, “User-based collaborative-filtering recommendation algorithms on hadoop,” in 3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010, pp. 478-481, 2010.
 
[28] M. Ramezani, P. Moradi, and F. Akhlaghian, “A pattern mining approach to enhance the accuracy of collaborative filtering in sparse data domains,” Physica A: Statistical Mechanics and its Applications, vol. 408, pp. 72-84, 2014.
 
[29] A. G. C. Pacheco, R. A. Krohling, and C. A. S. da Silva, “Restricted Boltzmann machine to determine the input weights for extreme learning machines,” Expert Systems with Applications, vol. 96, pp. 77-85, 2018.
 
[30] F. Han, H. F. Yao, and Q. H. Ling, “An improved evolutionary extreme learning machine based on particle swarm optimization,” Neurocomputing, vol. 116, pp. 87-93, 2013.
 
[31] G. Bin Huang, D. H. Wang, and Y. Lan, “Extreme learning machines: A survey,” International Journal of Machine Learning and Cybernetics, pp. 107–122, 2011.
 
[32] F. M. Harper and J. A. Konstan, “The movielens datasets: History and context,” ACM Transactions on Interactive Intelligent Systems, vol. 5, no. 19, pp. 1-19, 2015.
 
[33] H. Koohi and K. Kiani, “Two new collaborative filtering approaches to solve the sparsity problem,” Cluster Computing, pp. 753–765, 2020.
 
[34] R. Barzegar Nozari and H. Koohi, “A novel group recommender system based on members’ influence and leader impact,” Knowledge-Based Systems, vol. 205, 2020.
 
[35] H. Ben Yedder, U. Zakia, A. Ahmed, and L. Trajković, “Modeling prediction in recommender systems using restricted boltzmann machine,” in 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017, pp. 2063-2068, 2017.
 
[36] B. Alhijawi, G. Al-Naymat, N. Obeid, and A. Awajan, “Novel predictive model to improve the accuracy of collaborative filtering recommender systems,” Information Systems, vol. 96, 2021.
 
[37] M. Gupta, A. Thakkar, Aashish, V. Gupta, and D. P. S. Rathore, “Movie Recommender System Using Collaborative Filtering,” in Proceedings of the International Conference on Electronics and Sustainable Communication Systems, ICESC 2020, pp. 415-420, 2020.
 
[38] R. Barathy and P. Chitra, “Applying Matrix Factorization in Collaborative Filtering Recommender Systems,” in 2020 6th International Conference on Advanced Computing and Communication Systems, ICACCS 2020, pp. 635-639, 2020.
 
[39] M. R. I. Sarker and A. Matin, “A Hybrid Collaborative Recommendation System Based on Matrix Factorization and Deep Neural Network,” in 2021 International Conference on Information and Communication Technology for Sustainable Development, ICICT4SD 2021 - Proceedings, pp. 371-374, 2021.
 
[40] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques. In Data Mining: Concepts and Techniques, 2012.