Editing Recommender system (section)

==== Two-Tower Model ====

The Two-Tower model is a neural architecture<ref>Yi, X., Hong, L., Zhong, E., Tewari, A., & Dhillon, I. S. (2019). "A scalable two-tower model for estimating user interest in recommendations." ''Proceedings of the 13th ACM Conference on Recommender Systems''.</ref> commonly employed in large-scale recommendation systems, particularly for candidate retrieval tasks.<ref>Google Cloud Blog. \"Scaling Deep Retrieval with Two-Tower Models.\" Published November 30, 2022. [https://cloud.google.com/blog/products/ai-machine-learning/scaling-deep-retrieval-tensorflow-two-towers-architecture Accessed December 2024].</ref> It consists of two neural networks:

* '''User Tower''': Encodes user-specific features, such as interaction history or demographic data.
* '''Item Tower''': Encodes item-specific features, such as [[metadata]] or content embeddings.

The outputs of the two towers are fixed-length embeddings that represent users and items in a shared vector space. A similarity metric, such as [[dot product]] or [[cosine similarity]], is used to measure relevance between a user and an item.

This model is highly efficient for large datasets as embeddings can be pre-computed for items, allowing rapid retrieval during inference. It is often used in conjunction with ranking models for end-to-end recommendation pipelines.