Hybrid Method of Recommender System to Decrement Cold Start and Sparse Data Issues

Vahidy Rodpysh, K.; Mirabedini, Seyed Javad; Banirostam, T.

doi:10.22061/jecei.2021.7610.410

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	Hybrid Method of Recommender System to Decrement Cold Start and Sparse Data Issues
Journal of Electrical and Computer Engineering Innovations (JECEI)
مقاله 12، دوره 9، شماره 2، مهر 2021، صفحه 249-263 اصل مقاله (1.48 M)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2021.7610.410
نویسندگان
K. Vahidy Rodpysh؛ Seyed Javad Mirabedini^* ؛ T. Banirostam
Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
تاریخ دریافت: 27 خرداد 1399، تاریخ بازنگری: 08 آبان 1399، تاریخ پذیرش: 11 دی 1399
چکیده
Background and Objectives: The main purpose of the recommender system is to estimate the user's desire and provide a list of items predicted using the appropriate information. Recommender systems offering suggestions items to users face the two challenges of cold start and sparse data. Methods: This paper aims to propose a novel method to overcome the cold start and sparse data challenges in recommender systems. Singular value decomposition is one of the most common methods to reduce sparse data in recommender systems by reducing dimensions. However, the basic singular value decomposition can only extract the feature vectors of users and items, potentially resulting in lower recommendation precision. Using similarity criteria between entities, in addition to being able to reduce cold start that can solve the problem of singular value decomposition through extracting more refined factor vectors. Besides, considering the context dimensions as the third dimension of the matrix requires the use of another flexible algorithm, such as tensor factorization offering a viable solution to minimize the sparse data challenge. We propose a novel method named TCSSVD. First, a two-level matrix is obtained through similarity criteria between the user and the item to reduce the cold start challenge. The second step, to reduce the challenge of sparse data, contextual information is used with the help of tensor in the two level singular value decomposition. Results: Two data sets IMDB and STS due to the exerting of users feature, items feature and contextual information to review the proposed method. In order to the accuracy of the prediction of the criterion RMSE with an accuracy of 95%. However, since the user's rating of the item is of particular importance in the recommender systems. The method TCSSVD compared to the methods tensor factorization, HOSVD, BPR, and CTLSVD are used evaluation measure Precision, Recall, F1-score, and NDCG. Conclusion: The findings indicate that the use of innovative similarity criteria to extract user attributes, items to reduce the cold start and the use of contextual information through the tensor in the TCSSVD in order to sparse data. The results contribute to improving the accuracy of the suggestions given to users in recommender systems.
کلیدواژه‌ها
Recommender systems؛ Singular value decomposition؛ Context and Similarity criteria؛ Cold start؛ Sparse data

مراجع
[1] S.M. Abbas, K.A. Alam, S. Shamshirband, "A soft-rough set-based approach for handling contextual sparsity in context-aware video recommender systems," Mathematics, 7(8): 1-36, 2019. [2] M.Y.H. Al-Shamri, "User profiling approaches for demographic recommender systems," Knowledge Based Syst., 100: 175-187, 2016. [3] A.B. Barragáns-Martínez, E. Costa-Montenegro, J.C. Burguillo, M. Rey-López, F.A. Mikic-Fonte, A. Peleteiro, "A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition," Inf. Sci., 180(22): 4290-4311, 2010. [4] M. Braunhofer, "Hybridization techniques for cold-starting context-aware recommender systems," In Proc. 8th ACM Conference on Recommender systems: 405-408, 2014. [5] M. Braunhofer, M. Elahi, F. Ricci," User personality and the new user problem in a context-aware point of interest recommender system," in Proc. Information and communication technologies in tourism: 537-549, 2015. [6] G. Cai, W. Gu,"Heterogeneous context-aware recommendation algorithm with semi-supervised tensor factorization," in Proc. International Conference on Intelligent Data Engineering and Automated Learning: 232-241, 2017. [7] L.A.G. Camacho, S.N. Alves-Souza, "Social network data to alleviate cold-start in recommender system: A systematic review," Inform. Process. Manag., 54(4): 529-544, 2018. [8] Z.D. Champiri, S.R. Shahamiri, S.S.B. Salim, "A systematic review of scholar context-aware recommender systems," Expert Syst. Appl. 42(3): 1743-1758, 2015. [9] V. Codina, F. Ricci, L. Ceccaroni, "Local context modeling with semantic pre-filtering," in Proc. 7th ACM Conference on Recommender Systems: 363-366,2013. [10] L. Cui, W. Huang, Q. Yan, F.R. Yu, Z. Wen, N. Lu, "A novel context-aware recommendation algorithm with two-level SVD in social networks," Future Gener. Comput. Syst., 86: 1459-1470, 2018. [11] V.S. Dixit, P. Jain, "Recommendations with sparsity based weighted context framework," in Proc. International Conference on Computational Science and Its Applications (a): 289-305, 2018. [12] V.S. Dixit, P. Jain, "An improved similarity measure to alleviate the sparsity problem in context-aware recommender systems," in Proc. Towards Extensible and Adaptable Methods in Computing (b): 281-295, 2018. [13] A. Gautam, P. Chaudhary, K. Sindhwani, P. Bedi, "CBCARS: Content boosted context-aware recommendations using tensor factorization," in Proc. 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI): 75-81, 2016. [14] A. Gogna, A. Majumdar, "A comprehensive recommender system model: Improving accuracy for both warm and cold start users," IEEE Access, 3: 2803-2813, 2015. [15] X. Guan, C.T. Li, Y. Guan, "Matrix factorization with rating completion: An enhanced SVD model for collaborative filtering recommender systems," IEEE Access, 5: 27668-27678, 2017. [16] J. Herce-Zelaya, C. Porcel, J. Bernabé-Moreno, A. Tejeda-Lorente, E. Herrera-Viedma, "New technique to alleviate the cold-start problem in recommender systems using information from social media and random decision forests," Inf. Sci., 536: 156-170, 2020. [17] M. Hong, J.J. Jung, “Multi-sided recommendation based on social tensor factorization,” Inf. Sci., 447: 140-156, 2018. [18] Y. Hu, Q. Peng, X. Hu, "A time-aware and data sparsity tolerant approach for web service recommendation," in Proc. IEEE International Conference on Web Services: 33-40, 2014. [19] N. Idrissi, A. Zellou, "A systematic literature review of sparsity issues in recommender systems," Social Network Anal. Min., 10(1): 1-23, 2020. [20] R. Karimi, C. Freudenthaler, A. Nanopoulos, L. Schmidt-Thieme, "Exploiting the characteristics of matrix factorization for active learning in recommender systems," in Proc. the sixth ACM conference on Recommender systems: 317-320, 2012. [21] KDD IMDB dataset, 2020. [22] KDD STS dataset, 2020. [23] K.G. Saranya, G.S. Sadasivam, "A modified heuristic similarity measure for personalization using collaborative filtering technique," Appl. Math. Inf. Sci., 1: 307–315, 2017. [24] D. Kim, C. Park, J. Oh, S. Lee, H. Yu, "Convolutional matrix factorization for document context-aware recommendation" in Proc. 10th ACM conference on recommender systems: 233-240, 2016. [25] M. Kolahkaj, A. Harounabadi, A. Nikravanshalmani, R. Chinipardaz, "A hybrid context-aware approach for e-tourism package recommendation based on asymmetric similarity measurement and sequential pattern mining," Electron. Commerce Res. Appl., 42: 100978, 2020. [26] S. Kulkarni, S.F. Rodd, "Context-aware recommendation systems: a review of the state of the art techniques," Comput. Sci. Rev., 37: 100255, 2020. [27] LDOS-CoMoDa dataset, 2019. [28] X. Li, Z. Wang, L. Wang, R. Hu, Q. Zhu, "A multi-dimensional context-aware recommendation approach based on improved random forest algorithm," IEEE Access, 6: 45071-45085, 2018. [29] S. Linda, K.K. Bharadwaj, "A genetic algorithm approach to context-aware recommendations based on spatio-temporal aspects," in Proc. Integrated Intelligent Computing, Communication and Security: 59-70, 2019. [30] J. Liu, C. Wu, W. Liu, "Bayesian probabilistic matrix factorization with social relations and item contents for recommendation," Decis. Support Syst., 55(3): 838-850, 2013. [31] H. Liu, Z. Hu, A. Mian, H. Tian, X. Zhu, "A new user similarity model to improve the accuracy of collaborative filtering," Knowledge-Based Syst., 56: 156-166, 2014. [32] X. Liu, J. Zhang, C. Yan, "Towards context-aware collaborative filtering by learning context-aware latent representations," Knowledge-Based Syst., 199: 105988, 2020. [33] T. Mahara, "A new similarity measure based on mean measure of divergence for collaborative filtering in sparse environment," Procedia Comput. Sci., 89: 450-456, 201 6. [34] A.H. Natarajan, S. Vairavasundaram, S. Natarajan, A.H. Gandomi, "Resolving data sparsity and cold start problem in collaborative filtering recommender system using linked open data," Expert Syst. Appl., 149: 113248, 2020. [35] V.D. Nguyen, S. Sriboonchitta, V.N. Huynh, "Using community preference for overcoming sparsity and cold-start problems in collaborative filtering system offering soft ratings," Electron. Commerce Res. Appl., 26: 101-108, 2017. [36] M. Nilashi, O. bin Ibrahim, N. Ithnin," Multi-criteria collaborative filtering with high accuracy using higher order singular value decomposition and Neuro-Fuzzy system,” Knowledge-Based Syst., 60: 82-101, 2014. [37] V.A. Patil, D.J. Jayaswal, "Context relevancy assessment in tensor factorization-based recommender systems," in Proc. 3rd International Conference on Communication System, Computing and IT Applications (CSCITA): 141-145, 2020. [38] D. Rafailidis, P. Daras, "The TFC model: Tensor factorization and tag clustering for item recommendation in social tagging systems," IEEE Trans. Syst. Man Cybern.: Syst., 43(3): 673-688, 2012. [39] S.K. Raghuwanshi, P.R.K. Pateriya, "Accelerated singular value decomposition (asvd) using momentum based gradient descent optimization," J. King Saud Univ. Comput. Inf. Sci., 2018. [40] S. Raza, C. Ding, "Progress in context-aware recommender systems-an an overview," Comput. Sci. Rev., 31: 84-97, 2019. [41] M.S. Reddy, T. Adilakshmi, "Music recommendation system based on matrix factorization technique-SVD," in Proc. International Conference on Computer Communication and Informatics: 1-6, 2014. [42] X. Ren, M. Song, E. Haihong, J. Song, "Context-aware probabilistic matrix factorization modeling for point-of-interest recommendation," Neurocomputing, 241: 38-55, 2017. [43] S.Renjith, A. Sreekumar, M. Jathavedan, "An extensive study on the evolution of context-aware personalized travel recommender systems," Inf. Process. Manage., 57(1): 102078, 2020. [44] M. Rowe, "SemanticSVD++: incorporating semantic taste evolution for predicting ratings" in Proc. 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT): 213-220, 2014. [45] F. Ricci, L. Rokach, B. Shapira, In Recommender Systems Handbook, Springer, Boston, 1-35, 2011. [46] L. Safoury, A. Salah, "Exploiting user demographic attributes for solving cold-start problem in recommender system," Lect. Notes Software Eng., 1(3): 303-307, 2013. [47] Z.P. Shan, Y.Q. Lei, D.F. Zhang, J. Zhou," NASM: Nonlinearly attentive similarity model for recommendation system via locally attentive embedding," IEEE Access, 7: 70689-70700, 2019. [48] L.H. Son, "Dealing with the new user cold-start problem in recommender systems: A comparative review," Inf. Sys., 58: 87-104, 2016. [49] A.R. Sulthana, S. Ramasamy, "Ontology and context based recommendation system using neuro-fuzzy classification," Comput. Elec. Eng., 74: 498-510, 2019. [50] Z. Tan, L. He, " An efficient similarity measure for user-based collaborative filtering recommender systems inspired by the physical resonance principle," IEEE Access, 5: 27211-27228, 2017. [51] I. Viktoratos, A. Tsadiras, N. Bassiliades,"Combining community-based knowledge with association rule mining to alleviate the cold start problem in context-aware recommender systems," Expert Syst. Appl., 101: 78-90, 2018. [52] N.M. Villegas, C. Sánchez, J. Díaz-Cely, G. Tamura, "Characterizing context-aware recommender systems: A systematic literature review," Knowledge Based Sys., 140: 173-200, 2018. [53] M.G. Vozalis, K.G. Margaritis, "Using SVD and demographic data for the enhancement of generalized collaborative filtering," Inf. Sci., 177(15): 3017-3037, 2007. [54] S. Wang, C. Li, K. Zhao, H. Chen, "Learning to context-aware recommend with hierarchical factorization machines," Inf. Sci., 409: 121-138, 2017. [55] W. Wu, J. Zhao, C. Zhang, F. Meng, Z. Zhang, Y. Zhang, Q. Sun, "Improving the performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding," Knowledge Based Sys., 128: 71-77, 2017. [56] X. Xu, D. Yuan, "A novel matrix factorization recommendation algorithm fusing social trust and behaviors in micro-blogs," in Proc. 2017 IEEE 2nd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA): 283-287, 2017. [57] N. Yang, Y. Ma, L. Chen, S.Y. Philip, "A meta-feature based unified framework for both cold-start and warm-start explainable recommendations," World Wide Web, 23(1): 241-265, 2020. [58] X. Yuan, L. Han, S. Qian, G. Xu, H. Yan, "Singular value decomposition-based recommendation using imputed data," Knowledge Based Sys., 163: 485-494,2019. [59] A. Zahid, N.M. Sharef, A. Mustapha, "Normalization-based neighborhood model for cold start problem in the recommendation system," Int. Arab J. Inf. Technol, 17(3): 281-290, 2020. [60] Z. Zhang, Y. Zhang, Y. Ren, "Employing neighborhood reduction for alleviating sparsity and cold start problems in user-based collaborative filtering," Inf. Retr. J. , 23(4): 449-472, 2020. [61] Y. Zheng, B. Mobasher, R. Burke, "Similarity-based context-aware recommendation," in Proc. International Conference on Web Information Systems Engineering: 431-447, 2015.
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Hybrid Method of Recommender System to Decrement Cold Start and Sparse Data Issues