W. Wang and L. Guo, Agricultural big data and its application prospect, Jiangsu Agricultural Science, issue.09, pp.1-5, 2015.

H. Zhang, Z. Li, and T. Zou, A review of Agricultural big data, Computer Science, issue.S2, pp.387-392, 2014.

S. Ilarri, R. Hermoso, R. Trillo-lado, and M. Del-carmen-rodriguez-hernandez, A Review of the Role of Sensors in Mobile Context-Aware Recommendation Systems, INT J DISTRIB SENSN, issue.489264, 2015.

M. Unger, A. Bar, B. Shapira, and L. Rokach, Towards latent context-aware recommendation systems, KNOWL-BASED SYST, vol.104, pp.165-178, 2016.

S. Puglisi, J. Parra-arnau, J. Forne, and D. Rebollo-monedero, On content-based recommendation and user privacy in social-tagging systems, COMPUT STAND INTER, vol.41, pp.17-27, 2015.

M. Soares and P. Viana, Tuning metadata for better movie content-based recommendation systems, MULTIMED TOOLS APPL, vol.74, issue.17, pp.7015-7036, 2015.

T. Achakulvisut, D. E. Acuna, T. Ruangrong, and K. Kording, Science Concierge: A Fast Content-Based Recommendation System for Scientific Publications, PLOS ONE, vol.11, 2016.

Z. Yang and Z. Cai, Detecting abnormal profiles in collaborative filtering recommender systems, J INTELL INF SYST, vol.48, issue.3, pp.499-518, 2017.

D. Silva, E. Q. Camilo-junior, C. G. Pascoal, L. M. Rosa, and T. C. , An evolutionary approach for combining results of recommender systems techniques based on collaborative filtering, EXPERT SYST APPL, vol.53, pp.204-218, 2016.

Z. Yang, L. Xu, Z. Cai, and Z. Xu, Re-scale AdaBoost for attack detection in collaborative filtering recommender systems, KNOWL-BASED SYST, vol.100, pp.74-88, 2016.

L. H. Son, HU-FCF: A hybrid user-based fuzzy collaborative filtering method in Recommender Systems, EXPERT SYST APPL, vol.41, issue.15, pp.6861-6870, 2014.

Q. Liu, Y. Xiong, and W. Huang, Combining User-Based and Item-Based Models for Collaborative Filtering Using Stacked Regression, CHINESE J ELECTRON, vol.23, issue.4, pp.712-717, 2014.

P. Pirasteh, D. Hwang, and J. E. Jung, Weighted Similarity Schemes for High Scalability in User-Based Collaborative Filtering, MOBILE NETW APPL, vol.20, issue.4, pp.497-507, 2015.

F. Hao and R. H. Blair, A comparative study: classification vs. user-based collaborative filtering for clinical prediction, BMC MED RES METHODOL, vol.16, issue.172, 2016.

W. Ma, J. Shi, and R. Zhao, Normalizing Item-Based Collaborative Filter Using Context-Aware Scaled Baseline Predictor, MATH PROBL ENG, 2017.

Y. Du, C. Yao, S. Huo, and J. Liu, A new item-based deep network structure using a restricted Boltzmann machine for collaborative filtering, FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING, vol.18, issue.5, pp.658-666, 2017.

C. Li and K. He, CBMR: An optimized MapReduce for item-based collaborative filtering recommendation algorithm with empirical analysis, CONCURR COMP-PRACT E, vol.29, issue.e409210, 2017.

J. Zhang, Z. Su, D. Taber, J. Marsden, W. Moran et al., Predictors of eGFR Slope at One Year in Kidney Transplants -A Big Data Approach, AM J TRANSPLANT, vol.163, issue.SI, p.314, 2016.

S. Linda and K. K. Bharadwaj, A Fuzzy Trust Enhanced Collaborative Filtering for Effective Context-Aware Recommender Systems

C. Mettouris, A. P. Achilleos, and G. A. Papadopoulos, A Context Modelling System and Learning Tool for Context-Aware Recommender Systems

Z. Cheng and J. Shen, On Effective Location-Aware Music Recommendation, ACM T INFORM SYST, vol.34, issue.132, 2016.

L. Baltrunas and X. Amatriain, Towards time-dependent recommendation based on implicit feedback, Proc of RecSys'09 Workshop on CARS, 2009.

M. Van-setten, S. Pokraev, and J. Koolwaaij, Context-aware recommendations in the mobile tourist application COMPASS, ProcofAdaptiveHypermedia2004., LNCS3137.Berlin, pp.235-244, 2004.

D. Liu, X. Meng, and J. L. Chen, A framework for context-aware service recommendation

, Proc of IEEE ICACT, pp.2131-2134, 2008.