Abstract
Social media and other online sources generate abundance of data which leads to the problem of information overload. Recommender systems have emerged as a solution to deal with this problem which suggest items to users based on their information need. Despite the success of these systems, they also suffer from few issues such as cold start problem, sparsity, scalability, and overspecialization, among others. Although several attempts were made in the past to deal with these issues, several current recommender systems continue to face these issues. Deep learning algorithms can also be used to deal with recommendation system issues. In this paper, we discuss the issues of recommendation systems and how their impact can be alleviated through deep learning algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
S. Fitzek, “BOOK REVIEW, Cace Sorin, Fitzek Sebastian (Eds.): ICCV 2020 social report. Covid19 in Romania, data, analysis, evolutions and statistics, Bucharest: Romanian Academy, 2020, p. 273.“ Jurnalul Practicilor Comunitare Pozitive 20.3 (2020): 77–80
H. Wang, W. Fu, Personalized learning resource recommendation method based on dynamic collaborative filtering. Mob. Netw. Appl. 1–15 (2020)
B.A. Rogowsky, B.M. Calhoun, P. Tallal, Providing instruction based on students’ learning style preferences does not improve learning. Front. Psychol. 11, 164 (2020)
X. Wang et al., Optimizing data usage via differentiable rewards. in International Conference on Machine Learning. PMLR, (2020)
J.F. Chartier, P. Mongeau, J. Saint-Charles, Predicting semantic preferences in a socio-semantic system with collaborative filtering: A case study. Int. J. Inf. Manage. 51, 102020 (2020)
S. Natarajan et al., Resolving data sparsity and cold start problem in collaborative filtering recommender system using linked open data. Expert Syst. Appl. 149, 113248 (2020)
M.C. Brouwers et al., Development and validation of a tool to assess the quality of clinical practice guideline recommendations. JAMA Netw. open 3(5), e205535-e205535 (2020)
L. Wang et al., Diversified service recommendation with high accuracy and efficiency. Knowl.-Based Syst. 204, 106196 (2020)
M. Caron et al., Unsupervised learning of visual features by contrasting cluster assignments. arXiv preprint arXiv:2006.09882 (2020)
S.Y. Wang et al. CNN-generated images are surprisingly easy to spot... for now. in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2020)
A.A. Ewees et al., Improving multilayer perceptron neural network using chaotic grasshopper optimization algorithm to forecast iron ore price volatility. Resour. Policy 65, 101555 (2020)
Y. Luo, Z. Chen, T. Yoshioka, Dual-path rnn: efficient long sequence modeling for time-domain single-channel speech separation. in ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). (IEEE, 2020)
J. Ma et al., DDcGAN: A dual-discriminator conditional generative adversarial network for multi-resolution image fusion. IEEE Trans. Image Process. 29, 4980–4995 (2020)
C. Zheng et al., Gman: A graph multi-attention network for traffic prediction. in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34. No. 01 (2020)
R. Portelas et al., Automatic curriculum learning for deep rl: A short survey. arXiv preprint arXiv:2003.04664 (2020)
A. Onan, Sentiment analysis on product reviews based on weighted word embeddings and deep neural networks. Concurrency Comput. Pract. Experience e5909 (2020)
S.I. Mirzadeh, M. Farajtabar, H. Ghasemzadeh, Dropout as an implicit gating mechanism for continual learning. in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. (2020)
X. Lin, P.C. Boutros, Optimization and expansion of non-negative matrix factorization. BMC Bioinformatics 21(1), 1–10 (2020)
Z. Guo, H. Wang, A deep graph neural network-based mechanism for social recommendations. IEEE Trans. Industr. Inf. 17(4), 2776–2783 (2020)
M. Dai, W. Guo, X. Feng, Over-smoothing algorithm and its application to GCN semi-supervised classification. in International Conference of Pioneering Computer Scientists, Engineers and Educators. (Springer, Singapore, 2020)
R. Kiran, P. Kumar, B. Bhasker, DNNRec: A novel deep learning based hybrid recommender system. Expert Syst Appl 144, 113054 (2020)
N. Nassar, A. Jafar, Y. Rahhal, A novel deep multi-criteria collaborative filtering model for recommendation system. Knowl.-Based Syst. 187, 104811 (2020)
C. De Medio et al., MoodleREC: A recommendation system for creating courses using the moodle e-learning platform. Comput. Hum. Behav. 104, 106168 (2020)
X. Cai et al., A hybrid recommendation system with many-objective evolutionary algorithm. Expert Syst. Appl. 159, 113648 (2020)
A.M.A. Al-Sabaawi, H. Karacan, Y.E. Yenice, Exploiting implicit social relationships via dimension reduction to improve recommendation system performance. PloS One 154, e0231457 (2020)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rawat, B., Bist, A.S., Das, P., Samriya, J.K., Wariyal, S.C., Pandey, N. (2022). Alleviating the Issues of Recommendation System Through Deep Learning Techniques. In: Luhach, A.K., Poonia, R.C., Gao, XZ., Singh Jat, D. (eds) Second International Conference on Sustainable Technologies for Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1235. Springer, Singapore. https://doi.org/10.1007/978-981-16-4641-6_1
Download citation
DOI: https://doi.org/10.1007/978-981-16-4641-6_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-4640-9
Online ISBN: 978-981-16-4641-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)