Abstract
Social recommendation systems typically combine extra information like a social network with the user-item interaction network in order to alleviate data sparsity issues. This also helps in making more accurate and personalized recommendations. However, most of the existing systems work under the assumption that all socially connected users have equal influence on each other in a social network, which is not true in practice. Further, estimating the quantum of influence that exists among entities in a user-item interaction network is essential when only implicit ratings are available. This has been ignored even in many recent state-of-the-art models such as SAMN (Social Attentional Memory Network) and DeepSoR (Deep neural network model on Social Relations). Many a time, capturing a complex relationship between the entities (users/items) is essential to boost the performance of a recommendation system. We address these limitations by proposing a novel neural network model, SoRecGAT, which employs multi-head and multi-layer graph attention mechanism. The attention mechanism helps the model learn the influence of entities on each other more accurately. The proposed model also takes care of heterogeneity among the entities seamlessly. SoRecGAT is a general approach and we also validate its suitability when information in the form of a network of co-purchased items is available. Empirical results on eight real-world datasets demonstrate that the proposed model outperforms state-of-the-art models.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
- 4.
Throughout this paper, we refer to a user-user network (or connection) or a co-purchased item network (or connection) as a social network (or connection).
- 5.
Users/items present in a social network.
- 6.
- 7.
References
Abbasi, M.A., Tang, J., Liu, H.: Trust-aware recommender systems. In: Machine Learning Book on Computational Trust. Chapman & Hall/CRC Press (2014)
Balakrishnan, S., Chopra, S.: Collaborative ranking. In: WSDM, pp. 143–152. ACM (2012)
Chen, C., Zhang, M., Liu, Y., Ma, S.: Social attentional memory network: modeling aspect-and friend-level differences in recommendation. In: WSDM, pp. 177–185. ACM (2019)
Dong, Y., Chawla, N.V., Swami, A.: metapath2vec: scalable representation learning for heterogeneous networks. In: SIGKDD, pp. 135–144. ACM (2017)
Fan, W., Li, Q., Cheng, M.: Deep modeling of social relations for recommendation. In: AAAI (2018)
Fan, W., et al.: Graph neural networks for social recommendation. In: WWW, pp. 417–426 (2019)
Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning, vol. 1. MIT Press, Cambridge (2016)
Guo, G., Zhang, J., Yorke-Smith, N.: TrustSVD: collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. In: AAAI, vol. 15, pp. 123–125 (2015)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: WWW, pp. 173–182 (2017)
Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: RecSys, pp. 135–142. ACM (2010)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 8, 30–37 (2009)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)
Li, X., She, J.: Collaborative variational autoencoder for recommender systems. In: SIGKDD, pp. 305–314. ACM (2017)
Ma, H., Yang, H., Lyu, M.R., King, I.: SoRec: social recommendation using probabilistic matrix factorization. In: CIKM, pp. 931–940. ACM (2008)
Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: WSDM, pp. 287–296. ACM (2011)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Mnih, A., Salakhutdinov, R.R.: Probabilistic matrix factorization. In: Advances in Neural Information Processing Systems, pp. 1257–1264 (2008)
Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: SIGKDD, pp. 701–710. ACM (2014)
Rafailidis, D., Crestani, F.: Joint collaborative ranking with social relationships in top-N recommendation. In: CIKM, pp. 1393–1402. ACM (2016)
Rafailidis, D., Crestani, F.: Learning to rank with trust and distrust in recommender systems. In: RecSys, pp. 5–13. ACM (2017)
Rafailidis, D., Crestani, F.: Recommendation with social relationships via deep learning. In: SIGIR, pp. 151–158. ACM (2017)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 452–461. AUAI Press (2009)
Song, W., Xiao, Z., Wang, Y., Charlin, L., Zhang, M., Tang, J.: Session-based social recommendation via dynamic graph attention networks. In: WSDM, pp. 555–563. ACM (2019)
Sun, P., Wu, L., Wang, M.: Attentive recurrent social recommendation. In: SIGIR, pp. 185–194. ACM (2018)
Tang, J., Hu, X., Liu, H.: Social recommendation: a review. Soc. Netw. Anal. Min. 3(4), 1113–1133 (2013). https://doi.org/10.1007/s13278-013-0141-9
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (2018)
Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., Tan, T.: Session-based recommendation with graph neural networks. In: AAAI (2019)
Xue, H.J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, pp. 3203–3209 (2017)
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W.L., Leskovec, J.: Graph convolutional neural networks for web-scale recommender systems. In: SIGKDD, pp. 974–983. ACM (2018)
Zhang, S., Yao, L., Sun, A., Tay, Y.: Deep learning based recommender system: a survey and new perspectives. ACM Comput. Surv. (CSUR) 52(1), 5 (2019)
Zhao, T., McAuley, J., King, I.: Leveraging social connections to improve personalized ranking for collaborative filtering. In: CIKM, pp. 261–270. ACM (2014)
Zhou, C., et al.: ATRank: an attention-based user behavior modeling framework for recommendation. In: AAAI (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Vijaikumar, M., Shevade, S., Murty, M.N. (2020). SoRecGAT: Leveraging Graph Attention Mechanism for Top-N Social Recommendation. In: Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M., Robardet, C. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Lecture Notes in Computer Science(), vol 11906. Springer, Cham. https://doi.org/10.1007/978-3-030-46150-8_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-46150-8_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-46149-2
Online ISBN: 978-3-030-46150-8
eBook Packages: Computer ScienceComputer Science (R0)