Using Graph Neural Networks for Social Recommendations
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Published:2023-11-10
Issue:11
Volume:16
Page:515
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ISSN:1999-4893
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Container-title:Algorithms
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language:en
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Short-container-title:Algorithms
Author:
Tallapally Dharahas1, Wang John2, Potika Katerina1ORCID, Eirinaki Magdalini2ORCID
Affiliation:
1. Department of Computer Science, San José State University, San José, CA 95192, USA 2. Department of Computer Engineering, San José State University, San José, CA 95192, USA
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.
Funder
San José State University
Subject
Computational Mathematics,Computational Theory and Mathematics,Numerical Analysis,Theoretical Computer Science
Reference34 articles.
1. Ma, H., Zhou, D., Liu, C., Lyu, M.R., and King, I. (2011, January 9–12). Recommender Systems with Social Regularization. Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM ’11, Hong Kong, China. 2. Gulati, A., and Eirinaki, M. (2019, January 13–17). With a Little Help from My Friends (and Their Friends): Influence Neighborhoods for Social Recommendations. Proceedings of the World Wide Web Conference, WWW ’19, San Francisco, CA, USA. 3. Easley, D., and Kleinberg, J. (2012). Networks, Crowds, and Markets, Cambridge Books. 4. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., and Chua, T.S. (2017, January 3–7). Neural Collaborative Filtering. Proceedings of the 26th International Conference on World Wide Web, WWW’17, Perth, Australia. 5. Cheng, H.T., Koc, L., Harmsen, J., Shaked, T., Chandra, T., Aradhye, H., Anderson, G., Corrado, G., Chai, W., and Ispir, M. (2016, January 15). Wide & deep learning for recommender systems. Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, Boston, MA, USA.
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