A Generative Adversarial Approach with Social Relationship for Recommender Systems
Xiangxia Li, Kairui Chen, Yalan Zhou, Yanhong Chen · 2023
With the explosive growth of data, personalized recommendation systems have become an essential component in digitally-driven society. However, one of the main challenges faced by traditional recommendation algorithms is the difficulty in obtaining accurate user preferences, especially for users with limited historical interaction data., which ultimately impacts the performance of these methods. To address this challenge, the paper proposes a recommendation method, named STRGAN (Social trust relationships Generative Adversarial Network), it leverages the advantages of Generative Adversarial Networks (GANs) to tackle the data sparsity problem, by integrating user ratings and social relationships. By incorporating both types of information, STRGAN aims to improve the accuracy and quality of recommendations provided to users. Moreover, the proposed STRGAN model employs negative sampling techniques to ensure that the generated recommendations align with the real data. To evaluate the effectiveness of STRGAN, extensive experiments were conducted on the real world dataset FilmTrust. The empirical results demonstrate that STRGAN outperforms other GAN-based models in various evaluation metrics, such as precision, recall, normalized discounted cumulative gain (NDCG), and mean reciprocal rank (MRR). STRGAN offers a robust and efficient solution for personalized recommendation tasks. The results of experiment support the efficacy of STRGAN, indicating its potential to significantly improve recommendation accuracy in real-world applications.