论文标题
FELREC：在推荐系统中使用动态表示学习的有效处理项目冷启动
FELRec: Efficient Handling of Item Cold-Start With Dynamic Representation Learning in Recommender Systems
论文作者
论文摘要
每当新用户加入平台或将新项目添加到目录中时，推荐系统就会遇到冷启动问题。为了解决项目冷启动，我们建议用没有可学习权重的动态存储替换顺序推荐器中的嵌入层，并且可以保留任意数量的表示。在本文中，我们提出了FELREC，这是一个大型嵌入式网络，随着新信息的可用性，它以递归方式完善了用户和项目的现有表示形式。与类似方法相反，我们的模型代表了没有附带信息和耗时填充的新用户和项目，而是通过一系列现有表示形式来运行一个正向通行证。在项目冷启动期间，我们的方法的表现优于类似的方法29.50％-47.45％。此外，我们提出的模型可以很好地推广到以前看不见的数据集中的零照片设置。源代码可在https://github.com/kweimann/felrec上公开获得。
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 .
