Recommendation System Comparative Analysis: Internet of Things aided Networks
DOI:
https://doi.org/10.4108/eetiot.v8i29.1108Keywords:
Companion Recommendations, Privacy, Security, Social Networks SystemsAbstract
Today, the public is not willing to spend much time identifying their personal needs. Therefore, it needs a system that automatically recommends customized items to customers. The Recommender system has an internet of things (IoT) that entails a subclass of evidenced-based sieving structures that pursues to forecast the assessment of a customer would stretch to an item. Within social networks, numerous categories of RS operate on different recommendation expertise. In this state-of-the-art, we describe and classify current studies from three different aspects by describing different methods of recommender systems. The Friend Recommendation System in social networks is necessary and inevitable, and it is due to this kind of coordination that inevitably recommends latent friends to customers. Making recommendations for friends is an imperative assignment for community networks, as obligating supplementary networks customarily superiors to enhanced customer experience.
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References
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W. Shafik and S. A. Mostafavi, “Knowledge engineering on internet of things through reinforcement learning,” Int J Comput Appl, vol. 975, p. 8887, 2019.
W. Shafik, M. Matinkhah, and M. N. Sanda, “Network resource management drives machine learning: a survey and future research direction,” J. Commun. Technol. Electron. Comput. Sci., vol. 2020, pp. 1–15, 2020.
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S. M. Matinkhah and W. Shafik, “Smart grid empowered by 5G technology,” Smart Grid Conference, pp. 1–6, Tehran, Iran, 2019.
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A. Ratnaparkhe and V. Azad, “A study for companion recommendation based on user behavior,” International Journal of Research in Science, vol 13, pp. 7, 2017.
J. Gong, X. Gao, H. Cheng, J. Liu, Y. Song et al., “Integrating a weighted-average method into the random walk framework to generate individual companion recommendations,” Science China Information Sciences, vol. 60, no. 11, pp. 110104, 2017.
M. Xin and L. Wu, “Using multi-features to partition users for companions’ recommendation in location based social network,” Information Processing & Management, vol. 57, no. 1, pp. 102125, 2020.
M. Shao, W. Jiang and L. Zhang, “FRFP: A companion recommendation method based on fine-grained preference,” International Conference on Smart City and Informatization, Guangzhou, China, pp. 35-48, 2019, Springer, Singapore.
H. Ning, S. Dhelim and N. Aung, “Personet: companion recommendation system based on big-five personality traits and hybrid filtering,” IEEE Transactions on Computational Social Systems, vol. 6, no. 3, pp. 394-402, 2019.
N. Neehal and M. A. Mottalib, “Prediction of preferred personality for companion recommendation in social networks using artificial neural network,” International Conference on Electrical, Computer and Communication Engineering, pp. 1-6, 2019, IEEE.
Y. He, L. Wang, C. Mao, Y. Li, S. Sun et al., “Companion recommendation model based on multi-dimensional academic feature and attention mechanism,” Conference on Computer Supported Cooperative Work and Social Computing, pp. 472-484, 2019, Springer, Singapore.
S. Zhang, X. Li, H. Liu, Y. Lin and A. K. Sangaiah, “A privacy-preserving companion recommendation scheme in online social networks,” Sustainable cities and society, vol. 38, pp. 275-85, 2018.
Z. Yang, D. Li, R. Lin, Y. Tang, W. Li et al., “An academic social network companion recommendation algorithm based on decision tree,” IEEE Smart World, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation, pp. 1311-1316, 2018, IEEE.
C. Yang, T. Liu, L. Liu, X. Chen and Z. Hao, “A personalized Companion recommendation method combining network structure features and interaction information,” International Conference on Sensing and Imaging, pp. 267-274, 2018, Springer, Cham.
K. Xu, X. Zheng, Y. Cai, H. Min, Z. Gao et al., “Improving user recommendation by extracting social topics and interest topics of users in uni-directional social networks,” Knowledge-Based Systems, vol. 140, pp. 120-133, 2018.
X. Xiong, M. Zhang, J. Zheng and Y. Liu, “Social network user recommendation method based on dynamic influence,” International Conference on Web Information Systems and Applications, pp. 455-466, 2018, Springer, Cham.
D. Rafailidis and F. Crestani, “Companion recommendation in location-based social networks via deep pairwise learning,” IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 421-428, 2018. IEEE.
X. Ma, J. Ma, H. Li, Q. Jiang and S. Gao, “ARMOR: A trust-based privacy-preserving framework for decentralized companion recommendation in online social networks,” Future Generation Computer Systems, vol. 79, pp. 82-94, 2018.
X. Liu, Q. He, Y. Tian, W. C. Lee, J. McPherson et al., “Event-based social networks: linking the online and offline social worlds,” Proceedings of the 18th international conference on Knowledge discovery and data mining, pp. 1032-1040, 2012.
P. Kumar and G. R. Reddy, “Companionship recommendation system using topological structure of social networks,” Intelligent Computing Techniques: Theory, Practice, and Applications, pp. 237-246, 2018, Springer, Singapore.
M. Huang, B. Zhang, G. Zou, S. Cheng, Z. Zhou et al., “Companion recommendation in online social networks combining interest similarity and social interaction,” International Conference on Audio, Language and Image Processing, pp. 303-309, 2018, IEEE.
J. Gong, S. Chen, X. Gao, Y. Song and S. Wang, “Integrating LDA into the weighted average method for semantic companion recommendation,” Conference on Big Data, pp. 427-441, 2018, Springer, Singapore.
A. Lumbreras and R. Gavaldà, “Applying trust metrics based on user interactions to recommendation in social networks,” IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 1159-1164, 2012. IEEE.
M. B. Edith and W. Yu, “Companion recommendation system based on mobile data,” International Conference on Engineering Simulation and Intelligent Control, pp. 326-329, 2018. IEEE.
M. Duan, J. Huang, J. Zhang, Y. Tian, D. Li et al., “Companion recommendation system based on geolocation and contents of users,” International Conference on Computer Modeling, Simulation and Algorithm, 2018, Atlantis Press.
H. Cheng, M. Qian, Q. Li, Y. Zhou and T. Chen, “An efficient privacy-preserving companion recommendation scheme for social network,” IEEE Access, vol. 6, pp. 56018-56028, 2018.
W. Brendel, F. Han, L. Marujo, L. Jie, A. Korolova, “Practical privacy-preserving companion recommendations on social networks,” in Companion Proceedings of the the Web Conference, pp. 111-112, New York, NY, United States, 2018.
S. Shibao, J. Bowen, Z. Jingshan, Z. Yanan, Y. Xiaolong et al., “Companion recommendation algorithm based on fragmentation of time and transmission of interest,” International Journal of Computer Science Issues, vol. 14, no. 2, pp. 1, 2017.
H. Zheng and J. Wu, “Companion recommendation in online social networks: perspective of social influence maximization,” 26th International Conference on Computer Communication and Networks, pp. 1-9. IEEE.
Z. Zhang, X. Zhao and G. Wang, “FE-ELM: a new Companion recommendation model with extreme learning machine,” Cognitive Computation, vol. 9, no. 5, pp. 659-670, 2017.
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