Joint Modeling of Search and Recommendations Via an Unified Contextual Recommender (UniCoRn)
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866913473204584448 |
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| author | Bhattacharya, Moumita Ostuni, Vito Lamkhede, Sudarshan |
| author_facet | Bhattacharya, Moumita Ostuni, Vito Lamkhede, Sudarshan |
| contents | Search and recommendation systems are essential in many services, and they are often developed separately, leading to complex maintenance and technical debt. In this paper, we present a unified deep learning model that efficiently handles key aspects of both tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_10394 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Joint Modeling of Search and Recommendations Via an Unified Contextual Recommender (UniCoRn) Bhattacharya, Moumita Ostuni, Vito Lamkhede, Sudarshan Information Retrieval Artificial Intelligence Machine Learning Search and recommendation systems are essential in many services, and they are often developed separately, leading to complex maintenance and technical debt. In this paper, we present a unified deep learning model that efficiently handles key aspects of both tasks. |
| title | Joint Modeling of Search and Recommendations Via an Unified Contextual Recommender (UniCoRn) |
| topic | Information Retrieval Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2408.10394 |