Graph attention neural network social recommendation based on dynamic representation
Authors: 
Guang Wang, Danni Li, Yue SunAuthors Info & Claims
Guang Wang, Danni Li, Yue SunAuthors Info & ClaimsPages 83 - 88
Abstract
Although Few methods of deep learning have been proposed to deal with graphs that are dynamic in nature. Most of the existing recommendation systems based on social trust ignore the heterogeneous trust relationship between users and the heterogeneous interaction between users and products. And the appeal of a project can change over time, just as the dynamic interests of users are rarely considered, so is the relevance between projects. In order to overcome these limitations, a graph attention neural network Social recommendation (GNN-DR) model based on dynamic representation is proposed. Firstly, it considers the dynamic representation of users and items, and combines the influence of their relationship to model the short-term dynamic and long-term static interactive representation of item attractiveness. Second, the architecture systematically models user-user social diagrams and user-project diagrams, integrating heterogeneous trust and interactions. Finally, the attention mechanism is used to learn the potential factors of users and projects. Experiments on two real recommendation system datasets verify the validity of GNN-DR.
References
[1]
W. Zhao, H. Ma, Z. Li, X. Ao, and N. Li, “Improving social and behavior recommendations via network embedding,” Information Sciences, vol.516, pp. 125–141, 2020.
[2]
W. Fan, Q. Li, and M. Cheng, “Deep modeling of social relations for recommendation,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1, 2018.
[3]
S. Deng, L. Huang, G. Xu, X. Wu, and Z. Wu, “On deep learning for trust-aware recommendations in social networks,” IEEE transactions on neural networks and learning systems, vol. 28, no. 5, pp. 1164–1177,2016.
[4]
B. Yang, Y . Lei, J. Liu, and W. Li, “Social collaborative filtering by trust,” IEEE transactions on pattern analysis and machine intelligence,vol. 39, no. 8, pp. 1633–1647, 2016
[5]
Ruslan Salakhutdinov and Andriy Mnih. 2007. Probabilistic Matrix Factorization.In 21th Conference on Neural Information Processing Systems, Vol. 1. 2–1.
[6]
Jiliang Tang, Xia Hu, Huiji Gao, and Huan Liu. 2013. Exploiting local and global social context for recommendation. In IJCAI, Vol. 13. 2712–2718
[7]
Rendle, S., Freudenthaler, C., Gantner, Z., & Schmidt-Thieme, L. (2012). BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618.
[8]
Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 855–864.
[9]
A Semantic VSM-Based Recommender System," International Journal of Computer Theory and Engineering vol. 5, no. 2, pp. 331-336, 2013
[10]
A Prototype of a Recommended Nutrition and Energy Expenditure Application for Smartphones," International Journal of Computer Theory and Engineering vol. 8, no. 2, pp. 109-115, 2016.
[11]
A Personalized Product Based RecommendationnSystem Using Web Usage Mining and Semantic Web," International Journal of Computer Theory and Engineering vol. 4, no. 2, pp. 202-205, 2012.
[12]
Bo Yang, Yu Lei, Jiming Liu, and Wenjie Li. 2017. Social collaborative filtering by trust. IEEE transactions on pattern analysis and machine intelligence 39, 8 (2017),1633–1647
[13]
Federico Monti, Michael Bronstein, and Xavier Bresson. 2017. Geometric matrix completion with recurrent multi-graph neural networks. In Advances in Neural Information Processing Systems. 3700–3710.
[14]
Yang B, Lei Y, Liu J, Social collaborative filtering by trust[J]. IEEE transactions on pattern analysis and machine intelligence, 2016, 39(8): 1633-1647.
[15]
Wu, Le, "Diffnet++: A neural influence and interest diffusion network for social recommendation." IEEE Transactions on Knowledge and Data Engineering 34.10 (2020): 4753-4766.
[16]
Wu, Qitian, "Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems." The world wide web conference. 2019.
[17]
Song, Weiping, "Session-based social recommendation via dynamic graph attention networks." Proceedings of the Twelfth ACM international conference on web search and data mining. 2019.
Index Terms
- Graph attention neural network social recommendation based on dynamic representation
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Published In
December 2023
266 pages
ISBN:9798400709043
DOI:10.1145/3638985
Copyright © 2023 ACM.
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Published: 11 March 2024
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