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They are also common to help in software engineering tasks, including software modelling, where we are recently witnessing proposals to enrich modelling languages and environments with RSs. Modelling recommenders assist users in building models by suggesting items based on previous solutions to similar problems in the same domain. However, building a RS for a modelling language requires considerable effort and specialised knowledge. To alleviate this problem, we propose an automated, model-driven approach to create RSs for modelling languages. The approach provides a domain-specific language called <jats:sc>Droid<\/jats:sc> to configure every aspect of the RS: the type of the recommended modelling elements, the gathering and preprocessing of training data, the recommendation method, and the metrics used to evaluate the created RS. The RS so configured can be deployed as a service, and we offer out-of-the-box integration with Eclipse modelling editors. Moreover, the language is extensible with new data sources and recommendation methods. To assess the usefulness of our proposal, we report on two evaluations. The first one is an offline experiment measuring the precision, completeness and diversity of recommendations generated by several methods. The second is a user study \u2013 with 40 participants \u2013 to assess the perceived quality of the recommendations. The study also contributes with a novel evaluation methodology and metrics for RSs in model-driven engineering.<\/jats:p>","DOI":"10.1007\/s10664-024-10483-3","type":"journal-article","created":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T15:00:09Z","timestamp":1718722809000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Engineering recommender systems for modelling languages: concept, tool and evaluation"],"prefix":"10.1007","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2448-5570","authenticated-orcid":false,"given":"Lissette","family":"Almonte","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2818-2278","authenticated-orcid":false,"given":"Esther","family":"Guerra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6663-4231","authenticated-orcid":false,"given":"Iv\u00e1n","family":"Cantador","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9425-6362","authenticated-orcid":false,"given":"Juan","family":"de\u00a0Lara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,6,18]]},"reference":[{"key":"10483_CR1","doi-asserted-by":"crossref","unstructured":"Abid SB, Mahajan V, Lucio L (2019) Machine learning for learnability of MDD tools. 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