Forget Embedding Layers: Representation Learning for Cold-start in
Recommender Systems
- URL: http://arxiv.org/abs/2210.16928v1
- Date: Sun, 30 Oct 2022 19:08:38 GMT
- Title: Forget Embedding Layers: Representation Learning for Cold-start in
Recommender Systems
- Authors: Kuba Weimann and 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 or time-consuming fine-tuning.
Our proposed model generalizes well to previously unseen datasets.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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 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.
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