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Among other citation recommendation techniques, the widely used content-based filtering (CBF) exploits research articles\u2019 textual content to produce recommendations. However, CBF techniques are prone to the well-known cold-start problem. On the other hand, deep learning has shown its effectiveness in understanding the semantics of the text. The present paper proposes a citation recommendation system using deep learning models to classify rhetorical zones of the research articles and compute similarity using rhetorical zone embeddings that overcome the cold-start problem. Rhetorical zones are the predefined linguistic categories having some common characteristics about the text. A deep learning model is trained using ART and CORE datasets with an accuracy of 76 per cent. The final ranked lists of the recommendations have an average of 0.704 normalized discounted cumulative gain (nDCG) score involving ten domain experts. The proposed system is applicable for both local and global context-aware recommendations.<\/jats:p>","DOI":"10.1007\/s12652-022-03899-6","type":"journal-article","created":{"date-parts":[[2022,5,28]],"date-time":"2022-05-28T09:03:03Z","timestamp":1653728583000},"page":"419-433","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A deep learning approach for context-aware citation recommendation using rhetorical zone classification and similarity to overcome cold-start problem"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9536-0065","authenticated-orcid":false,"given":"Muhammad Azeem","family":"Abbas","sequence":"first","affiliation":[]},{"given":"Saheed","family":"Ajayi","sequence":"additional","affiliation":[]},{"given":"Muhammad","family":"Bilal","sequence":"additional","affiliation":[]},{"given":"Ade","family":"Oyegoke","sequence":"additional","affiliation":[]},{"given":"Maruf","family":"Pasha","sequence":"additional","affiliation":[]},{"given":"Hafiz Tauqeer","family":"Ali","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,28]]},"reference":[{"key":"3899_CR1","doi-asserted-by":"publisher","first-page":"106428","DOI":"10.1016\/j.knosys.2020.106428","volume":"208","author":"WA Abro","year":"2020","unstructured":"Abro WA, Qi G, Ali Z, Feng Y, Aamir M (2020) Multi-turn intent determination and slot filling with neural networks and regular expressions. 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