Skip to main content
main-content
Top

Hint

Swipe to navigate through the chapters of this book

2021 | OriginalPaper | Chapter

Social Recommendation for Social Networks Using Deep Learning Approach: A Systematic Review

Authors : Muhammad Alrashidi, Ali Selamat, Roliana Ibrahim, Ondrej Krejcar

Published in: Advances in Computational Collective Intelligence

Publisher: Springer International Publishing

share
SHARE

Abstract

The increasing popularity of social networks indicates that the vast amounts of data contained within them could be useful in various implementations, including recommendation systems. Interests and research publications on deep learning-based recommendation systems have largely increased. This study aimed to identify, summarize, and assess studies related to the application of deep learning-based recommendation systems on social media platforms to provide a systematic review of recent studies and provide a way for further research to improve the development of deep learning-based recommendation systems in social environments. A total of 32 papers were selected from previous studies in five of the major digital libraries, including Springer, IEEE, ScienceDirect, ACM, Scopus, and Web of Science, published between 2016 and 2020. Results revealed that even though RS has received high coverage in recent years, several obstacles and opportunities will shape the future of RS for researchers. In addition, social recommendation systems achieving high accuracy can be built by using a combination of techniques that incorporate a range of features in SRS. Therefore, the adoption of deep learning techniques in developing social recommendation systems is undiscovered.
Literature
1.
go back to reference Ricci, F., Rokach, L., Shapira, B.: Recommender Systems Handbook (2011) Ricci, F., Rokach, L., Shapira, B.: Recommender Systems Handbook (2011)
10.
go back to reference Review, A.S.: Applied sciences recommender system based on temporal models, pp. 1–27 (2020) Review, A.S.: Applied sciences recommender system based on temporal models, pp. 1–27 (2020)
11.
go back to reference Del Carpio, A.F., Angarita, L.B.: Trends in software engineering processes using deep learning: a systematic literature review. In: 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), pp. 445–454. IEEE (2020) Del Carpio, A.F., Angarita, L.B.: Trends in software engineering processes using deep learning: a systematic literature review. In: 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), pp. 445–454. IEEE (2020)
12.
go back to reference Zhou, X., Jin, Y., Zhang, H., Li, S., Huang, X.: A map of threats to validity of systematic literature reviews in software engineering. In: 2016 23rd Asia-Pacific Software Engineering Conference (APSEC), pp. 153–160. IEEE (2016) Zhou, X., Jin, Y., Zhang, H., Li, S., Huang, X.: A map of threats to validity of systematic literature reviews in software engineering. In: 2016 23rd Asia-Pacific Software Engineering Conference (APSEC), pp. 153–160. IEEE (2016)
20.
go back to reference Zheng, L., et al.: MARS: memory attention-aware recommender system. In: 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 11–20. IEEE (2019) Zheng, L., et al.: MARS: memory attention-aware recommender system. In: 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 11–20. IEEE (2019)
23.
go back to reference Garg, D., Gupta, P., Malhotra, P., Vig, L., Shroff, G.: Sequence and time aware neighborhood for session-based recommendations. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1069–1072. ACM, New York (2019) Garg, D., Gupta, P., Malhotra, P., Vig, L., Shroff, G.: Sequence and time aware neighborhood for session-based recommendations. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1069–1072. ACM, New York (2019)
24.
go back to reference Chen, H., Li, J.: Adversarial tensor factorization for context-aware recommendation. In: Proceedings of the 13th ACM Conference on Recommender Systems, pp. 363–367. ACM, New York (2019) Chen, H., Li, J.: Adversarial tensor factorization for context-aware recommendation. In: Proceedings of the 13th ACM Conference on Recommender Systems, pp. 363–367. ACM, New York (2019)
26.
go back to reference Song, W., Xiao, Z., Wang, Y., Charlin, L., Zhang, M., Tang, J.: Session-based social recommendation via dynamic graph attention networks. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 555–563. ACM, New York (2019) Song, W., Xiao, Z., Wang, Y., Charlin, L., Zhang, M., Tang, J.: Session-based social recommendation via dynamic graph attention networks. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 555–563. ACM, New York (2019)
27.
go back to reference Wu, X., Shi, B., Dong, Y., Huang, C., Chawla, N. V.: Neural tensor factorization for temporal interaction learning. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 537–545. ACM, New York (2019) Wu, X., Shi, B., Dong, Y., Huang, C., Chawla, N. V.: Neural tensor factorization for temporal interaction learning. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 537–545. ACM, New York (2019)
28.
go back to reference Qu, Z., Li, B., Wang, X., Yin, S., Zheng, S.: An efficient recommendation framework on social media platforms based on deep learning. In: 2018 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 599–602. IEEE (2018) Qu, Z., Li, B., Wang, X., Yin, S., Zheng, S.: An efficient recommendation framework on social media platforms based on deep learning. In: 2018 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 599–602. IEEE (2018)
29.
go back to reference Lu, Y., Dong, R., Smyth, B.: Coevolutionary recommendation model. In: Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW 2018, pp. 773–782. ACM Press, New York (2018) Lu, Y., Dong, R., Smyth, B.: Coevolutionary recommendation model. In: Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW 2018, pp. 773–782. ACM Press, New York (2018)
31.
go back to reference Neammanee, T., Maneeroj, S.: Time-aware recommendation based on user preference driven. In: 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), pp. 26–31. IEEE (2018) Neammanee, T., Maneeroj, S.: Time-aware recommendation based on user preference driven. In: 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), pp. 26–31. IEEE (2018)
32.
go back to reference Niu, W., Caverlee, J., Lu, H.: Neural personalized ranking for image recommendation. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 423–431. ACM, New York (2018) Niu, W., Caverlee, J., Lu, H.: Neural personalized ranking for image recommendation. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 423–431. ACM, New York (2018)
33.
go back to reference Liang, D., Krishnan, R.G., Hoffman, M.D., Jebara, T.: Variational autoencoders for collaborative filtering. In: Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW 2018, pp. 689–698. ACM Press, New York (2018) Liang, D., Krishnan, R.G., Hoffman, M.D., Jebara, T.: Variational autoencoders for collaborative filtering. In: Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW 2018, pp. 689–698. ACM Press, New York (2018)
36.
go back to reference Dang, Q.-V., Ignat, C.-L.: dTrust: a simple deep learning approach for social recommendation. In: 2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC), pp. 209–218. IEEE (2017) Dang, Q.-V., Ignat, C.-L.: dTrust: a simple deep learning approach for social recommendation. In: 2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC), pp. 209–218. IEEE (2017)
38.
go back to reference Zheng, L., Noroozi, V., Yu, P.S.: Joint deep modeling of users and items using reviews for recommendation. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pp. 425–434. ACM, New York (2017) Zheng, L., Noroozi, V., Yu, P.S.: Joint deep modeling of users and items using reviews for recommendation. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pp. 425–434. ACM, New York (2017)
39.
go back to reference Wang, X., et al.: Dynamic attention deep model for article recommendation by learning human editors’ demonstration. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2051–2059. ACM, New York (2017) Wang, X., et al.: Dynamic attention deep model for article recommendation by learning human editors’ demonstration. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2051–2059. ACM, New York (2017)
40.
go back to reference Cao, S., Yang, N., Liu, Z.: Online news recommender based on stacked auto-encoder. In: 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS), pp. 721–726. IEEE (2017) Cao, S., Yang, N., Liu, Z.: Online news recommender based on stacked auto-encoder. In: 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS), pp. 721–726. IEEE (2017)
41.
go back to reference Hidasi, B., Quadrana, M., Karatzoglou, A., Tikk, D.: Parallel recurrent neural network architectures for feature-rich session-based recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 241–248. ACM, New York (2016) Hidasi, B., Quadrana, M., Karatzoglou, A., Tikk, D.: Parallel recurrent neural network architectures for feature-rich session-based recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 241–248. ACM, New York (2016)
42.
go back to reference Tan, J., Wan, X., Xiao, J.: A neural network approach to quote recommendation in writings. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 65–74. ACM, New York (2016) Tan, J., Wan, X., Xiao, J.: A neural network approach to quote recommendation in writings. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 65–74. ACM, New York (2016)
43.
go back to reference Lee, H., Ahn, Y., Lee, H., Ha, S., Lee, S.: Quote recommendation in dialogue using deep neural network. In: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 957–960. ACM, New York (2016) Lee, H., Ahn, Y., Lee, H., Ha, S., Lee, S.: Quote recommendation in dialogue using deep neural network. In: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 957–960. ACM, New York (2016)
Metadata
Title
Social Recommendation for Social Networks Using Deep Learning Approach: A Systematic Review
Authors
Muhammad Alrashidi
Ali Selamat
Roliana Ibrahim
Ondrej Krejcar
Copyright Year
2021
DOI
https://doi.org/10.1007/978-3-030-88113-9_2

Premium Partner