# Using Graph Neural Networks for Social Recommendations
## 5 Conclusions and Discussion  
This paper proposes RelationalNet, a novel neural influence and diffusion algorithm for social recommendations based on Diffnet++ [13]. RelationalNet improves upon Diffnet++ with the addition of item-item and item-user interactions modeled as graphs used in the GNNs. By incorporating not only user-user relations in the form of the social graph (similar to other social recommender systems) but also item-item relations that are independent of any user interactions, RelationalNet addresses the cold-start problem of little or no user-item ratings. Furthermore, RelationalNet utilizes a multi-layer diffusion network that employs graph attention to combine graph and node-level representations, managing to capture the latent features of each graph. The experimental evaluation showed that the RelationalNet algorithm achieves better performance in generating top-\(n\) recommendations (with a 11.8% improvement of HR and 6.2% improvement of NDCG on average) as opposed to the current SOTA and predecessor, Diffnet++, showing the potential of such extensions that enhance the input with additional connections.  
There is still significant scope to explore, like the different mechanisms for forming connections between items, calculating user and item similarities, exploring higher-order connections [31, 32] instead of edges, and investigating graph reasoning algorithms to learn users' preferences better. In addition, we plan to leverage our previous work on influential users [2, 23, 33, 34] to refine the user-user similarities and create neighborhoods of influence. Perhaps one of the main shortcomings of the proposed approach is the overhead that is introduced by adding one additional graph and expanding the social network beyond direct neighbors, as the RelationalNet algorithm was 25% slower than the DiffNet++ algorithm. The biggest cost factor to consider if we want to make our algorithm scalable is to restrict the attention modules in the nodes and in the graphs. We can achieve this by reducing the dimensions of the embeddings for the user and item, respectively. One of the future ideas is to use subgraph sampling for the training. However, selecting representative subgraphs that will preserve good graph characteristics needs further investigation, especially in our case, where we have four graphs. While using subgraph sampling will increase the scalability of the model, this comes with the cost of accuracy and expressability of the model.  
Overall, the RelationalNet algorithm provides a significant step forward in social recommendation systems by taking advantage of social connections, item correlations, and leveraging the power of GNNs.  
Conceptualization, D.T. and K.P.; methodology, D.T.; software, D.T.; formal analysis, D.T. and K.P.; investigation, D.T., K.P. and M.E.; validation, J.W.; resources, D.T. and J.W.; data curation, D.T. and J.W.; writing--original draft preparation, D.T., K.P. and M.E.; writing--review and editing, J.W., K.P. and M.E.; visualization, D.T.; supervision, K.P. and M.E.; funding acquisition, K.P.; project administration K.P. All authors have read and agreed to the published version of the manuscript.  
Research reported in this publication was supported by the Division of Research and Innovation at San Jose State University under Award Number 22-RSG-08-034 for [PERSON]. The content is solely the responsibility of the authors and does not necessarily represent the official views of San Jose State University.  
Publicky available datasets were analyzed in this study. This data can be found here: [[https://www.yelp.com/dataset](https://www.yelp.com/dataset)]([https://www.yelp.com/dataset](https://www.yelp.com/dataset)), accessed on 1 October 2022.  
The authors declare no conflict of interest.