{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T16:09:18Z","timestamp":1783613358693,"version":"3.55.0"},"reference-count":34,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:00:00Z","timestamp":1699574400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"San Jos\u00e9 State University","award":["22-RSG-08-034"],"award-info":[{"award-number":["22-RSG-08-034"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>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\u2013item, and user\u2013user relationships but also item\u2013item relationships with graphs and uses them as input to the recommendation process. The rationale for utilizing item\u2013item 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.<\/jats:p>","DOI":"10.3390\/a16110515","type":"journal-article","created":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T02:46:47Z","timestamp":1699843607000},"page":"515","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Using Graph Neural Networks for Social Recommendations"],"prefix":"10.3390","volume":"16","author":[{"given":"Dharahas","family":"Tallapally","sequence":"first","affiliation":[{"name":"Department of Computer Science, San Jos\u00e9 State University, San Jos\u00e9, CA 95192, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, San Jos\u00e9 State University, San Jos\u00e9, CA 95192, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0332-1347","authenticated-orcid":false,"given":"Katerina","family":"Potika","sequence":"additional","affiliation":[{"name":"Department of Computer Science, San Jos\u00e9 State University, San Jos\u00e9, CA 95192, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4711-3366","authenticated-orcid":false,"given":"Magdalini","family":"Eirinaki","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, San Jos\u00e9 State University, San Jos\u00e9, CA 95192, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ma, H., Zhou, D., Liu, C., Lyu, M.R., and King, I. 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