FELRec: Efficient Handling of Item Cold-Start With Dynamic Representation Learning in Recommender Systems

• URL: http://arxiv.org/abs/2210.16928v2
• Date: Tue, 01 Oct 2024 14:39:12 GMT
• Title: FELRec: Efficient Handling of Item Cold-Start With Dynamic Representation Learning in Recommender Systems
• Authors: Kuba Weimann, Tim O. F. Conrad,
• Abstract summary: We present FELRec, a large embedding network that refines the existing representations of users and items. In contrast to similar approaches, our model represents new users and items without side information and time-consuming finetuning. Our proposed model generalizes well to previously unseen datasets in zero-shot settings.
• Score: 0.0
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• Abstract: 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 .

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