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 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%-47.45%. Further, our proposed model generalizes well to previously unseen datasets in zero-shot settings. The source code is publicly available at https://github.com/kweimann/FELRec .

翻译：推荐系统在新用户加入平台或新物品加入目录时均面临冷启动问题。为应对物品冷启动，本文提出将序列推荐器中的嵌入层替换为无学习权重且可保持任意数量表征的动态存储机制。本文提出的FELRec是一种大型嵌入网络，能够在新信息可用时以递归方式优化用户与物品的现有表征。相较于同类方法，本模型无需辅助信息与耗时的微调过程即可表征新用户与物品，仅需对现有表征序列执行单次前向传播。在物品冷启动场景下，本方法性能较同类方法提升29.50%-47.45%。此外，所提模型在零样本设置下对未见数据集展现出良好泛化能力。源代码已公开于https://github.com/kweimann/FELRec。