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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.16073 |
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| _version_ | 1866929265488953344 |
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| author | Gebre, Binyam Ranta, Karoliina Elzen, Stef van den Kuiper, Ernst Baars, Thijs Heskes, Tom |
| author_facet | Gebre, Binyam Ranta, Karoliina Elzen, Stef van den Kuiper, Ernst Baars, Thijs Heskes, Tom |
| contents | In personalized recommender systems, embeddings are often used to encode customer actions and items, and retrieval is then performed in the embedding space using approximate nearest neighbor search. However, this approach can lead to two challenges: 1) user embeddings can restrict the diversity of interests captured and 2) the need to keep them up-to-date requires an expensive, real-time infrastructure. In this paper, we propose a method that overcomes these challenges in a practical, industrial setting. The method dynamically updates customer profiles and composes a feed every two minutes, employing precomputed embeddings and their respective similarities. We tested and deployed this method to personalise promotional items at Bol, one of the largest e-commerce platforms of the Netherlands and Belgium. The method enhanced customer engagement and experience, leading to a significant 4.9% uplift in conversions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_16073 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Pfeed: Generating near real-time personalized feeds using precomputed embedding similarities Gebre, Binyam Ranta, Karoliina Elzen, Stef van den Kuiper, Ernst Baars, Thijs Heskes, Tom Information Retrieval Artificial Intelligence Machine Learning H.3.3 In personalized recommender systems, embeddings are often used to encode customer actions and items, and retrieval is then performed in the embedding space using approximate nearest neighbor search. However, this approach can lead to two challenges: 1) user embeddings can restrict the diversity of interests captured and 2) the need to keep them up-to-date requires an expensive, real-time infrastructure. In this paper, we propose a method that overcomes these challenges in a practical, industrial setting. The method dynamically updates customer profiles and composes a feed every two minutes, employing precomputed embeddings and their respective similarities. We tested and deployed this method to personalise promotional items at Bol, one of the largest e-commerce platforms of the Netherlands and Belgium. The method enhanced customer engagement and experience, leading to a significant 4.9% uplift in conversions. |
| title | Pfeed: Generating near real-time personalized feeds using precomputed embedding similarities |
| topic | Information Retrieval Artificial Intelligence Machine Learning H.3.3 |
| url | https://arxiv.org/abs/2402.16073 |