Tensors in Modern Search and Product Discovery

What Are Tensors and Why They Matter for Modern Search?

Modern digital commerce systems increasingly rely on vector representations to capture semantic meaning in search and recommendation. Tensors extend this concept.

A vector is a single-dimensional collection of numbers that allows you to represent the semantics of a single concept, such as a product name or a user interest. Tensors extend this concept to multiple dimensions, allowing you to represent, retrieve, and compute with richer representations of products and users. 

For example, tensors allow factors such as brands, styles, and utility to be represented as separate vectors, along with individual user preferences for those factors. These signals can then be combined flexibly at ranking time, producing high-quality results at scale without compressing all product and user characteristics into a single embedding vector.

For Search & Product Discovery, this is important because modern relevance is no longer based on a single signal. It depends on blending lexical matching, semantic intent, personalization, inventory constraints, and business rules simultaneously.

Vespa’s native tensor engine allows these signals to be evaluated together in real time, directly within the serving layer. This enables:

• Multi-signal ranking without external inference systems
• Efficient execution of machine-learned models
• Fine-grained control over hybrid search blending
• Scalable personalization across large catalogs

In practice, tensors provide the mathematical foundation for combining hybrid search, algorithmic merchandising, and personalization in a single production-grade system.

While many platforms offer vector search as a feature, Vespa’s tensor engine enables organizations to combine all relevance signals, not just embeddings, into a unified ranking model.