RePlay: a Recommendation Framework for Experimentation and Production Use
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866917793841020928 |
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| author | Vasilev, Alexey Volodkevich, Anna Kulandin, Denis Bysheva, Tatiana Klenitskiy, Anton |
| author_facet | Vasilev, Alexey Volodkevich, Anna Kulandin, Denis Bysheva, Tatiana Klenitskiy, Anton |
| contents | Using a single tool to build and compare recommender systems significantly reduces the time to market for new models. In addition, the comparison results when using such tools look more consistent. This is why many different tools and libraries for researchers in the field of recommendations have recently appeared. Unfortunately, most of these frameworks are aimed primarily at researchers and require modification for use in production due to the inability to work on large datasets or an inappropriate architecture. In this demo, we present our open-source toolkit RePlay - a framework containing an end-to-end pipeline for building recommender systems, which is ready for production use. RePlay also allows you to use a suitable stack for the pipeline on each stage: Pandas, Polars, or Spark. This allows the library to scale computations and deploy to a cluster. Thus, RePlay allows data scientists to easily move from research mode to production mode using the same interfaces. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_07272 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RePlay: a Recommendation Framework for Experimentation and Production Use Vasilev, Alexey Volodkevich, Anna Kulandin, Denis Bysheva, Tatiana Klenitskiy, Anton Information Retrieval Machine Learning Software Engineering Using a single tool to build and compare recommender systems significantly reduces the time to market for new models. In addition, the comparison results when using such tools look more consistent. This is why many different tools and libraries for researchers in the field of recommendations have recently appeared. Unfortunately, most of these frameworks are aimed primarily at researchers and require modification for use in production due to the inability to work on large datasets or an inappropriate architecture. In this demo, we present our open-source toolkit RePlay - a framework containing an end-to-end pipeline for building recommender systems, which is ready for production use. RePlay also allows you to use a suitable stack for the pipeline on each stage: Pandas, Polars, or Spark. This allows the library to scale computations and deploy to a cluster. Thus, RePlay allows data scientists to easily move from research mode to production mode using the same interfaces. |
| title | RePlay: a Recommendation Framework for Experimentation and Production Use |
| topic | Information Retrieval Machine Learning Software Engineering |
| url | https://arxiv.org/abs/2409.07272 |