  Recommender systems suffer from the cold-start problem whenever a new user
joins the platform or a new item is added to the catalog. 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 FELRec, 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 or time-consuming fine-tuning. During item cold-start, our method
outperforms similar method by 29.50%-47.45%. Further, our proposed model
generalizes well to previously unseen datasets. The source code is publicly
available at github.com/kweimann/FELRec.
