{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T22:30:58Z","timestamp":1783117858389,"version":"3.54.6"},"reference-count":77,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T00:00:00Z","timestamp":1702944000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Big Data"],"abstract":"<jats:p>By providing personalized suggestions to users, recommender systems have become essential to numerous online platforms. Collaborative filtering, particularly graph-based approaches using Graph Neural Networks (GNNs), have demonstrated great results in terms of recommendation accuracy. However, accuracy may not always be the most important criterion for evaluating recommender systems' performance, since beyond-accuracy aspects such as recommendation diversity, serendipity, and fairness can strongly influence user engagement and satisfaction. This review paper focuses on addressing these dimensions in GNN-based recommender systems, going beyond the conventional accuracy-centric perspective. We begin by reviewing recent developments in approaches that improve not only the accuracy-diversity trade-off but also promote serendipity, and fairness in GNN-based recommender systems. We discuss different stages of model development including data preprocessing, graph construction, embedding initialization, propagation layers, embedding fusion, score computation, and training methodologies. Furthermore, we present a look into the practical difficulties encountered in assuring diversity, serendipity, and fairness, while retaining high accuracy. Finally, we discuss potential future research directions for developing more robust GNN-based recommender systems that go beyond the unidimensional perspective of focusing solely on accuracy. This review aims to provide researchers and practitioners with an in-depth understanding of the multifaceted issues that arise when designing GNN-based recommender systems, setting our work apart by offering a comprehensive exploration of beyond-accuracy dimensions.<\/jats:p>","DOI":"10.3389\/fdata.2023.1251072","type":"journal-article","created":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T10:51:07Z","timestamp":1702983067000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":32,"title":["Beyond-accuracy: a review on diversity, serendipity, and fairness in recommender systems based on graph neural networks"],"prefix":"10.3389","volume":"6","author":[{"given":"Tomislav","family":"Duricic","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dominik","family":"Kowald","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emanuel","family":"Lacic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elisabeth","family":"Lex","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,12,19]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1905.01986","article-title":"Beyond personalization: research directions in multistakeholder recommendation","author":"Abdollahpouri","year":"2019","journal-title":"arXiv"},{"key":"B2","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1145\/3450613.3456821","article-title":"\u201cUser-centered evaluation of popularity bias in recommender systems,\u201d","author":"Abdollahpouri","year":"2021","journal-title":"Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization"},{"key":"B3","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-29659-3","volume-title":"Recommender Systems, Volume 1","author":"Aggarwal","year":"2016"},{"key":"B4","first-page":"83","article-title":"\u201cSerendipity into session-based recommendation: Focusing on unexpectedness, relevance, and usefulness of recommendations,\u201d","author":"Boo","year":"2023","journal-title":"Companion Proceedings of the 28th International Conference on Intelligent User Interfaces"},{"key":"B5","first-page":"603","article-title":"\u201cNovelty and diversity in recommender systems,\u201d","volume-title":"Recommender Systems Handbook","author":"Castells","year":"2021"},{"key":"B6","doi-asserted-by":"publisher","author":"Chen","year":"2023","DOI":"10.48550\/arXiv.2305.14886"},{"key":"B7","article-title":"\u201cFast greedy map inference for determinantal point process to improve recommendation diversity,\u201d","author":"Chen","year":"2018","journal-title":"Advances in Neural Information Processing Systems"},{"key":"B8","doi-asserted-by":"publisher","first-page":"3301","DOI":"10.3390\/electronics11203301","article-title":"A comparative analysis of bias amplification in graph neural network approaches for recommender systems","volume":"11","author":"Chizari","year":"2022","journal-title":"Electronics"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2204.08570","article-title":"A comprehensive survey on trustworthy graph neural networks: privacy, robustness, fairness, and explainability","author":"Dai","year":"2022","journal-title":"arXiv"},{"key":"B10","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1145\/3437963.3441752","article-title":"\u201cSay no to the discrimination: Learning fair graph neural networks with limited sensitive attribute information,\u201d","author":"Dai","year":"2021","journal-title":"Proceedings of the 14th ACM International Conference on Web Search and Data Mining"},{"key":"B11","first-page":"1","article-title":"Fairness in recommender systems: research landscape and future directions","author":"Deldjoo","year":"2023","journal-title":"User Modeling and User-Adapted Interaction"},{"key":"B12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3439729","article-title":"A survey on adversarial recommender systems: from attack\/defense strategies to generative adversarial networks","volume":"54","author":"Deldjoo","year":"2021","journal-title":"ACM Comp. 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