--- title: Understanding the recommender system's two-tower model description: The two-tower model architecture uses dual neural networks to create shared embedding spaces for users and products, enabling product recommendations. The... canonical: https://daily.dev/posts/understanding-the-recommender-system-s-two-tower-model-hnnxils5v twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Understanding the recommender system's two-tower model | daily.dev og:description: The two-tower model architecture uses dual neural networks to create shared embedding spaces for users and products, enabling product recommendations. The... og:url: https://daily.dev/posts/understanding-the-recommender-system-s-two-tower-model-hnnxils5v og:image: https://api.daily.dev/og/posts/hNNXiLs5v.png og:image:alt: Understanding the recommender system's two-tower model og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Understanding the recommender system's two-tower model **[Red Hat Developer](https://daily.dev/sources/rhdev)** · 11 min read · 0 upvotes · 0 comments ## Summary The two-tower model architecture uses dual neural networks to create shared embedding spaces for users and products, enabling product recommendations. The training pipeline leverages OpenShift AI with KFP to orchestrate data loading, model training, and candidate generation stages. Each tower processes entity features through feedforward networks, producing 64-component embeddings that are compared using Euclidean distance against interaction magnitude scores. The architecture handles cold start problems for new users through category-based preferences and addresses data sharing challenges between pipeline components using S3-compatible storage. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Build an AI-driven product recommender with OpenShift AI](https://daily.dev/posts/build-an-ai-driven-product-recommender-with-openshift-ai-ehias2ty4) · Red Hat Developer · 1 upvotes · 0 comments - [Building Real-Time Recommendations with Snowflake Online Feature Serving — Two-Tower Networks](https://daily.dev/posts/building-real-time-recommendations-with-snowflake-online-feature-serving-two-tower-networks-mqwgcqwal) · Snowflake Community · 1 upvotes · 0 comments - [Deploying a Multistage Multimodal Recommender System on Amazon Elastic Kubernetes Service](https://daily.dev/posts/deploying-a-multistage-multimodal-recommender-system-on-amazon-elastic-kubernetes-service-bvj2ecnkv) · Towards Data Science · 4 upvotes · 0 comments - [AI-generated product review summaries with OpenShift AI](https://daily.dev/posts/ai-generated-product-review-summaries-with-openshift-ai-i1lrcqe7v) · Red Hat Developer · 1 upvotes · 0 comments - [Personalized Restaurant Ranking with a Two-Tower Embedding Variant](https://daily.dev/posts/personalized-restaurant-ranking-with-a-two-tower-embedding-variant-hexnnwxgq) · Towards Data Science · 1 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#pytorch](https://daily.dev/tags/pytorch), [#openshift](https://daily.dev/tags/openshift), [#mlops](https://daily.dev/tags/mlops) [View this post on daily.dev](https://daily.dev/posts/understanding-the-recommender-system-s-two-tower-model-hnnxils5v) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Understanding the recommender system's two-tower model","url":"https://daily.dev/posts/understanding-the-recommender-system-s-two-tower-model-hnnxils5v","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/understanding-the-recommender-system-s-two-tower-model-hnnxils5v"},"datePublished":"2026-01-26T14:27:17.439Z","dateModified":"2026-01-26T14:27:57.356Z","description":"The two-tower model architecture uses dual neural networks to create shared embedding spaces for users and products, enabling product recommendations. 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