Publication Date
11-1-2023
Document Type
Article
Publication Title
Algorithms
Volume
16
Issue
11
DOI
10.3390/a16110515
Abstract
Recommender systems have revolutionized the way users discover and engage with content. Moving beyond the collaborative filtering approach, most modern recommender systems leverage additional sources of information, such as context and social network data. Such data can be modeled using graphs, and the recent advances in Graph Neural Networks have led to the prominence of a new family of graph-based recommender system algorithms. In this work, we propose the RelationalNet algorithm, which not only models user–item, and user–user relationships but also item–item relationships with graphs and uses them as input to the recommendation process. The rationale for utilizing item–item interactions is to enrich the item embeddings by leveraging the similarities between items. By using Graph Neural Networks (GNNs), RelationalNet incorporates social influence and similar item influence into the recommendation process and captures more accurate user interests, especially when traditional methods fall short due to data sparsity. Such models improve the accuracy and effectiveness of recommendation systems by leveraging social connections and item interactions. Results demonstrate that RelationalNet outperforms current state-of-the-art social recommendation algorithms.
Funding Number
22-RSG-08-034
Keywords
graph neural networks, influence diffusion, recommender systems, social network, social recommendation algorithm
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Computer Science; Computer Engineering
Recommended Citation
Dharahas Tallapally, John Wang, Katerina Potika, and Magdalini Eirinaki. "Using Graph Neural Networks for Social Recommendations" Algorithms (2023). https://doi.org/10.3390/a16110515
