FELRec: Efficient Handling of Item Cold-Start With Dynamic Representation Learning in Recommender Systems
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| _version_ | 1866914961546018816 |
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| author | Weimann, Kuba Conrad, Tim O. F. |
| author_facet | Weimann, Kuba Conrad, Tim O. F. |
| contents | 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 . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2210_16928 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | FELRec: Efficient Handling of Item Cold-Start With Dynamic Representation Learning in Recommender Systems Weimann, Kuba Conrad, Tim O. F. Information Retrieval Machine Learning 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 . |
| title | FELRec: Efficient Handling of Item Cold-Start With Dynamic Representation Learning in Recommender Systems |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2210.16928 |