Version 1
: Received: 13 September 2022 / Approved: 14 September 2022 / Online: 14 September 2022 (16:09:15 CEST)
How to cite:
Tzeng, L.; Shih, J.; Liou, R.; Huang, D.; Smolen, D. A Graph Neural Network Recommendation Method Based on Social Relationship Embedding. Preprints2022, 2022090212. https://doi.org/10.20944/preprints202209.0212.v1
Tzeng, L.; Shih, J.; Liou, R.; Huang, D.; Smolen, D. A Graph Neural Network Recommendation Method Based on Social Relationship Embedding. Preprints 2022, 2022090212. https://doi.org/10.20944/preprints202209.0212.v1
Tzeng, L.; Shih, J.; Liou, R.; Huang, D.; Smolen, D. A Graph Neural Network Recommendation Method Based on Social Relationship Embedding. Preprints2022, 2022090212. https://doi.org/10.20944/preprints202209.0212.v1
APA Style
Tzeng, L., Shih, J., Liou, R., Huang, D., & Smolen, D. (2022). A Graph Neural Network Recommendation Method Based on Social Relationship Embedding. Preprints. https://doi.org/10.20944/preprints202209.0212.v1
Chicago/Turabian Style
Tzeng, L., Dely Huang and Dan Smolen. 2022 "A Graph Neural Network Recommendation Method Based on Social Relationship Embedding" Preprints. https://doi.org/10.20944/preprints202209.0212.v1
Abstract
There is a considerable amount of research in online social networks, most of which focuses on the structural analysis of social graphs. The interpersonal relationships of social networks, especially friend circle, can solve the cold start and sparsity problems, and through the relationship between social networks can effectively recommend users' favorite items (items), such as music , videos, brands/products, preferred tags, location, services, etc. User relationships in social networks are diverse and there are many different perspectives on different social networks. Associations among users can form multi-layered composite networks, and multi-layered social networks present new challenges and opportunities. Different relationships can influence users' preferences to different degrees, which in turn affects their behavior. Therefore, fusing multiple social networks is an effective way to improve recommendation. Although some studies have started to address multiple social network recommendations, simple linear superposition cannot reflect the coupling and nonlinear association between multiple social networks. In this paper, we propose a graph neural network recommendation model under social relationships based on this background. We first propose to compute the 2nd order collaborative signals and their intensities directly from the neighboring matrix for updating the node embedding of the graph convolution layer. Secondly by embedding historical evaluations, various social networks constituting different dimensions, the attention integration of user preferences by different social networks is achieved, and its effectiveness and scalability are demonstrated in theoretical derivation and experimental validation. The theoretical derivation and experimental validation demonstrate its effectiveness and scalability.
Keywords
Graph Neural Network; Recommendation; Social Relationship
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
