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In the area of academia, there is a need to help researchers discover the most appropriate and relevant scientific information through recommendations. Nevertheless, we argue that there is a major gap between academic state-of-the-art RS and real-world problems. In this paper, we present a novel multi-staged RS based on clustering, graph modeling and deep learning that manages to run on a full dataset (scientific digital library) in the magnitude of millions users and items (papers). We run several tests (experiments\/evaluation) as a means to find the best approach regarding the tuning of our system; so, we present and compare three versions of our RS regarding recall and NDCG metrics. The results show that a multi-staged RS that utilizes a variety of techniques and algorithms is able to face real-world problems and large academic datasets. In this way, we suggest a way to close or minimize the gap between research and industry value RS.\n<\/jats:p>","DOI":"10.1007\/s10115-024-02094-7","type":"journal-article","created":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T06:01:56Z","timestamp":1713420116000},"page":"4463-4496","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["An academic recommender system on large citation data based on clustering, graph modeling and deep learning"],"prefix":"10.1007","volume":"66","author":[{"given":"Vaios","family":"Stergiopoulos","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Vassilakopoulos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eleni","family":"Tousidou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio","family":"Corral","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,4,18]]},"reference":[{"key":"2094_CR1","doi-asserted-by":"publisher","unstructured":"Stergiopoulos V, Vassilakopoulos M, Tousidou E, Corral A (2022) Hyper-parameters tuning of artificial neural networks: An application in the field of recommender systems. 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