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To address item cold-start, we propose to replace the embedding layer in sequential recommenders with a dynamic storage that has no learnable weights and can keep an arbitrary number of representations. In this paper, we present , a large embedding network that refines the existing representations of users and items in a recursive manner, as new information becomes available. In contrast to similar approaches, our model represents new users and items without side information and time-consuming finetuning, instead it runs a single forward pass over a sequence of existing representations. During item cold-start, our method outperforms similar method by 29.50\u201347.45%. Further, our proposed model generalizes well to previously unseen datasets in zero-shot settings. The source code is publicly available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/kweimann\/FELRec\" ext-link-type=\"uri\">https:\/\/github.com\/kweimann\/FELRec<\/jats:ext-link>.<\/jats:p>","DOI":"10.1007\/s41060-024-00635-5","type":"journal-article","created":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T02:02:03Z","timestamp":1728266523000},"page":"2937-2950","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FELRec: efficient handling of item cold-start with dynamic representation learning in recommender systems"],"prefix":"10.1007","volume":"20","author":[{"given":"Kuba","family":"Weimann","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tim O. 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