SoRecGAT: Leveraging Graph Attention Mechanism for Top-N Social Recommendation

Vijaikumar, M and Shevade, S and Murty, MN (2020) SoRecGAT: Leveraging Graph Attention Mechanism for Top-N Social Recommendation. In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019, 16-20 September 2019, Wurzburg; Germany, pp. 430-446.

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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. © Springer Nature Switzerland AG 2020.

Item Type:	Conference Paper
Publication:	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher:	Springer
Additional Information:	cited By 0; Conference of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019 ; Conference Date: 16 September 2019 Through 20 September 2019; Conference Code:239479
Keywords:	Deep learning; Deep neural networks; Learning systems; Recommender systems, Attention mechanisms; Complex relationships; Interaction networks; Multi-layer graphs; Neural network model; Novel neural network; Personalized recommendation; Real-world datasets, Multilayer neural networks
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	04 Nov 2020 09:51
Last Modified:	04 Nov 2020 09:51
URI:	http://eprints.iisc.ac.in/id/eprint/65640

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