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Recommender systems suffer from the cold-start problem whenever a new userjoins the platform or a new item is added to the catalog. To address itemcold-start, we propose to replace the embedding layer in sequentialrecommenders with a dynamic storage that has no learnable weights and can keepan arbitrary number of representations. In this paper, we present FELRec, alarge embedding network that refines the existing representations of users anditems in a recursive manner, as new information becomes available. In contrastto similar approaches, our model represents new users and items without sideinformation or time-consuming fine-tuning. During item cold-start, our methodoutperforms similar method by 29.50%-47.45%. Further, our proposed modelgeneralizes well to previously unseen datasets. The source code is publiclyavailable at github.com/kweimann/FELRec.

Forget Embedding Layers: Representation Learning for Cold-start in Recommender Systems