Abstract
Recommender systems helps the user to recommend something based on their interest. As online data is growing extensively, we require recommender systems more, and there has a lot of work going on in this field. Recommender system in online video streaming platforms plays a huge role. 80% of videos watched online are from recommendations. There are many techniques on which this system works. As most of the information is in graph structure and graph neural networks (GNNs) have a specialty in representation learning, the field of utilizing GNN in recommender systems is expanding. This chapter provides knowledge of GNN-based recommender systems and state-of-the-art models in developing this domain.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Singh, R., Maurya, S., Tripathi, T., Narula, T., Srivastav, G.: Movie recommendation system using cosine similarity and knn, pp. 2249–8958 (2020)
Kumar, M., Yadav, D., Singh, A., Gupta, V.K.: Article: a movie recommender system: Movrec. Int. J. Comput. Appl. 124, 7–11 (2015)
de Campos, L.M., Fernández-Luna, J.M., Huete, J.F., Rueda-Morales, M.A.: Combining content-based and collaborative recommendations: a hybrid approach based on bayesian networks. Int. J. Approx. Reason. 51(7), 785–799 (2010)
Harpreet Kaur Virk, E.A.S., Maninder Singh, Er.: Analysis and design of hybrid online movie recommender system. In: International Journal of Innovations in Engineering and Technology (2015)
Gupta, U., Patil, N.: Recommender system based on hierarchical clustering algorithm chameleon. In: International Advance Computing Conference (IACC) (2015)
Furtado, Singh, A.: Movie recommendation system usingmachine learning. Int. J. Res. Industrialeng. 9(1), 84–98 (2020)
Uluyagmur, M., Cataltepe, Z., Tayfur, E.: Content-based movie recommendation using different feature sets. In: Proceedings of the World Congress on Engineering and Computerscience, vol. 1, pp. 17–24 (2012)
Subramaniyaswamy et al.: A personalised movie recommendation system based on collaborative filtering. Int. J. High Perform. Comput. Netw. 10(1–2), 54–63 (2017)
Ahuja, R., Solanki, A., Nayyar, A.: Movierecommender system using K-Means clustering and K-NearestNeighbor. In: 2019 9th International Conference on Cloud Computing,Data Science & Engineering (Confluence). IEEE, pp. 263–268.6 (2019)
Hug, N.: Surprise: a python library for recommender systems. J. Open Source Softw. 5, 2174 (2020)
Manimurugan, S., Almutairi, S.: A user-based video recommendation approach using CAC filtering, PCA with LDOS-CoMoDa. In: The Journal of Supercomputing, pp. 1–15 (2022)
Duan, R., Jiang, C., Jain, H.K.: Combining review-based collaborative filtering and matrix factorization: a solution to rating’s sparsity problem. In: Decision Support Systems, p. 113748 (2022)
Wu, S., et al.: Graph neural networks in recommender systems: a survey (2020). arXiv:2011.02260
hen Gao et al.: Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions (2021). arXiv:2109.12843
He, X., et al.: Lightgcn: simplifying and powering graph convolution network for recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (2020)
Wei, Y., et al.: MMGCN: multi-modal graph convolution network for personalized recommendation of video. In: Proceedings of the 27th ACM International Conference on Multimedia (2019)
Wang, Q., et al.: DualGNN: dual graph neural network for multimedia recommendation. In: IEEE Transactions on Multimedia (2021)
Liu, Q., et al.: Graph neural network for tag ranking in tag-enhanced video recommendation. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management (2020)
Liu, Y., et al.: Concept-aware denoising graph neural network for video recommendation. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management (2021)
Hamilton, W., Ying Z., Leskovec J.: Inductive representation learning on large graphs. Adv. Neural Inf. Process. Syst. 30 (2017)
Wei, Y., et al.: Graph-refined convolutional network for multimedia recommendation with implicit feedback. In: Proceedings of the 28th ACM International Conference on Multimedia (2020)
Song, J., et al.: NGAT4Rec: Neighbor-Aware Graph Attention Network For Recommendation (2020). arXiv:2010.12256
Sang, L., et al.: Context-dependent propagating-based video recommendation in multimodal heterogeneous information networks. IEEE Trans. Multimed. 23, 2019–2032 (2020)
Lund, J., Ng, Y.-K.: Movie recommendations using the deeplearning approach. In: 2018 IEEE International Conference on Information Reuse and Integration (IRI). IEEE, pp. 47–54 (2018)
Purificato, E., Wehnert, S., De Luca, E.W.: Dynamic privacy-preserving recommendations on academic graph data. Computers 10(9), 107 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Rawka, T., Jadeja, M. (2022). A Survey on Graph Neural Network Based Video Recommendation System. In: Hong, TP., Serrano-Estrada, L., Saxena, A., Biswas, A. (eds) Deep Learning for Social Media Data Analytics. Studies in Big Data, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-031-10869-3_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-10869-3_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-10868-6
Online ISBN: 978-3-031-10869-3
eBook Packages: Computer ScienceComputer Science (R0)