Acknowledgement
The work was supported by grants from the Nature Science Foundation of Anhui Province in China (No. 2008085MF193 and 1908085MF194), the Natural Science Research Foundation of the Education, Department of Anhui Province of China (No. KJ2019A0578); the Outstanding Young Talents Program of Anhui Province (No. gxyqZD2018060).
References
- A. Montarnal, W. Mu, F. Benaben, J. Lamothe, M. Lauras, and N. Salatge, "Automated deduction of cross-organizational collaborative business processes," Information Sciences, vol. 453, pp. 30-49, 2018. https://doi.org/10.1016/j.ins.2018.03.041
- P. Chamoso, A. Rivas, S. Rodriguez, and J. Bajo, "Relationship recommender system in a business and employment-oriented social network," Information Sciences, vol. 433, pp. 204-220, 2018. https://doi.org/10.1016/j.ins.2017.12.050
- P. Resnick and H. R. Varian, "Recommender systems," Communications of the ACM, vol. 40, no. 3, pp. 56-58, 1997. https://doi.org/10.1145/245108.245121
- N. Idrissi and A. Zellou, "A systematic literature review of sparsity issues in recommender systems," Social Network Analysis and Mining, vol. 10, no. 1, article no. 15, 2020. https://doi.org/10.1007/s13278-020-0626-2
- D. Goldberg, D. Nichols, B. M. Oki, and D. Terry, "Using collaborative filtering to weave an information tapestry," Communications of the ACM, vol. 35, no. 12, pp. 61-70, 1992. https://doi.org/10.1145/138859.138867
- C. Li and K. He, "CBMR: An optimized MapReduce for item-based collaborative filtering recommendation algorithm with empirical analysis," Concurrency and Computation: Practice and Experience, vol. 29, no. 10, article no e4092, 2017. https://doi.org/10.1002/cpe.4092
- E. Shmueli and T. Tassa, "Secure multi-party protocols for item-based collaborative filtering," in Proceedings of the 11th ACM Conference on Recommender Systems, Como, Italy, 2017, pp. 89-97.
- X. Wang, Y. Zhong, L. Zhang, and Y. Xu, "Spatial group sparsity regularized nonnegative matrix factorization for hyperspectral unmixing," IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 11, pp. 6287-6304, 2017. https://doi.org/10.1109/TGRS.2017.2724944
- G. Guo, J. Zhang, D. Thalmann, and N. Yorke-Smith, "Leveraging prior ratings for recommender systems in e-commerce," Electronic Commerce Research and Applications, vol. 13, no. 6, pp. 440-455, 2014. https://doi.org/10.1016/j.elerap.2014.10.003
- Y. Hu, Q. Peng, X. Hu, and R. Yang, "Time aware and data sparsity tolerant web service recommendation based on improved collaborative filtering," IEEE Transactions on Services Computing, vol. 8, no. 5, pp. 782-794, 2014. https://doi.org/10.1109/TSC.2014.2381611
- N. R. Kermany and S. H. Alizadeh, "A hybrid multi-criteria recommender system using ontology and neuro-fuzzy techniques," Electronic Commerce Research and Applications, vol. 21, pp. 50-64, 2017. https://doi.org/10.1016/j.elerap.2016.12.005
- C. Feng, J. Liang, P. Song, and Z. Wang, "A fusion collaborative filtering method for sparse data in recommender systems," Information Sciences, vol. 521, pp. 365-379, 2020. https://doi.org/10.1016/j.ins.2020.02.052