Suggest, Complement, Inspire: Story of Two Tower Recommendations at Allegro.com
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909723919384576 |
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| author | Osowska-Kurczab, Aleksandra Nazarko, Klaudia Marzec, Mateusz Wojciechowska, Lidia Kremeňová, Eliška |
| author_facet | Osowska-Kurczab, Aleksandra Nazarko, Klaudia Marzec, Mateusz Wojciechowska, Lidia Kremeňová, Eliška |
| contents | Building large-scale e-commerce recommendation systems requires addressing three key technical challenges: (1) designing a universal recommendation architecture across dozens of placements, (2) decreasing excessive maintenance costs, and (3) managing a highly dynamic product catalogue. This paper presents a unified content-based recommendation system deployed at Allegro.com, the largest e-commerce platform of European origin. The system is built on a prevalent Two Tower retrieval framework, representing products using textual and structured attributes, which enables efficient retrieval via Approximate Nearest Neighbour search. We demonstrate how the same model architecture can be adapted to serve three distinct recommendation tasks: similarity search, complementary product suggestions, and inspirational content discovery, by modifying only a handful of components in either the model or the serving logic. Extensive A/B testing over two years confirms significant gains in engagement and profit-based metrics across desktop and mobile app channels. Our results show that a flexible, scalable architecture can serve diverse user intents with minimal maintenance overhead. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_03702 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Suggest, Complement, Inspire: Story of Two Tower Recommendations at Allegro.com Osowska-Kurczab, Aleksandra Nazarko, Klaudia Marzec, Mateusz Wojciechowska, Lidia Kremeňová, Eliška Information Retrieval Machine Learning H.3.3 Building large-scale e-commerce recommendation systems requires addressing three key technical challenges: (1) designing a universal recommendation architecture across dozens of placements, (2) decreasing excessive maintenance costs, and (3) managing a highly dynamic product catalogue. This paper presents a unified content-based recommendation system deployed at Allegro.com, the largest e-commerce platform of European origin. The system is built on a prevalent Two Tower retrieval framework, representing products using textual and structured attributes, which enables efficient retrieval via Approximate Nearest Neighbour search. We demonstrate how the same model architecture can be adapted to serve three distinct recommendation tasks: similarity search, complementary product suggestions, and inspirational content discovery, by modifying only a handful of components in either the model or the serving logic. Extensive A/B testing over two years confirms significant gains in engagement and profit-based metrics across desktop and mobile app channels. Our results show that a flexible, scalable architecture can serve diverse user intents with minimal maintenance overhead. |
| title | Suggest, Complement, Inspire: Story of Two Tower Recommendations at Allegro.com |
| topic | Information Retrieval Machine Learning H.3.3 |
| url | https://arxiv.org/abs/2508.03702 |