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Vector Embeddings from Clickstream Data
Clickstream data captures how users search, browse, and interact online, offering a detailed view of real-world behavior. At Datos, we use vector embeddings to structure this data in a way that enables large-scale similarity modeling, faster information retrieval, and better representation of user intent.
This report outlines how we build and evaluate embeddings, the requirements we apply for production use, and how they support tasks such as semantic search, recommendations, predictive modeling, and more.
This report covers:
- How we build vector embeddings from clickstream data to capture real-world behavior and intent
- Our approach to modeling similarity, critical for powering search, recommendations, and trend analysis
- The requirements we set for production-ready embeddings that scale across millions of users and queries
- Use cases where these embeddings deliver measurable impact in personalization, audience analysis, and more
- How we train, evaluate, and validate embeddings to ensure they perform in real-world applications
This report will be particularly useful for:
- AI developers and machine learning teams
- Vector database engineers and architects
- Retail and e-commerce platforms
- Marketing, SEO, and content strategy professionals
- Analytics providers and data science teams
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