SoURA: a user-reliability-aware social recommendation system based on graph neural network

Article Type

Research Article

Publication Title

Neural Computing and Applications

Abstract

Exploiting user trust information for developing a recommendation system has gained increasing research interest in recent years. Due to the exchange of opinions about items over the social network, trust plays a crucial role for a user to like or dislike an item. Graph Neural Networks (GNNs), which have the intrinsic power of integrating node information and topological structure, have a high potential to advance the field of trust-aware social recommendation. However, as of now, this area is little explored, with most of the existing GNN-based models ignoring the trust propagation and trust composition properties. To address this issue, in this paper, we propose a novel GNN-based framework that can capture such trust propagation and trust composition aspects by incorporating the concept of ‘user-reliability.’ Our proposed user-reliability-aware social recommendation framework, termed as SoURA, generates the user-embedding and item-embedding with consideration to the user-reliability values, which, in turn, helps in better evaluation of the user trust. Experimental evaluations on the benchmark Ciao and Epinion datasets demonstrate the effectiveness of incorporating user-reliability for finding user-embedding and item embedding in a social recommendation system. The proposed SoURA is found to show a minimum of 25% improvement over the state-of-the-art GNN-based recommendation algorithms.

First Page

18533

Last Page

18551

DOI

https://10.1007/s00521-023-08679-7

Publication Date

9-1-2023

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