Enhancing review-based user representation on learned social graph for recommendation

In recent years, review-based methods have been widely used to learn user representations because reviews contain abundant information. However, few users would like to write reviews for items. The data sparsity problem in reviews has become an emerging challenge in the field. Meanwhile, other approaches resort to social information based on graph neural networks (GNNs) to augment user representation. However, the efficacy of these approaches is always jeopardized because social graphs are not available in most real-world scenarios. Therefore, we propose a new Enhancing Review-based User Representation Model on Learned Social Graph for Recommendation, named ERUR. Specifically, we first introduce a review encoder to model review-based user/item representations. Second, we design a graph learning network to learn social relations between users according to the review-based user representation. Third, a graph neural network is developed to augment the final user representation under the supervision of a generative adversarial network. It integrates user reviews and social relations to enrich the final user representation for recommendation and further alleviate the data sparsity problem in reviews. Finally, we conduct experiments on seven datasets to demonstrate the effectiveness of the ERUR model in user representation learning compared to the SOTA recommendation models. (C) 2023 Elsevier B.V. All rights reserved.