Why online stores keep showing the wrong products — and why tensors fix it

Product discovery must combine many signals such as keywords, embeddings, behavior, inventory and promotions.

Vector search matches items by semantic similarity but does not natively balance multiple structured signals.

Tensor-based ranking represents signals in multidimensional form so a single model can evaluate their interactions.

Building tensor support into the search platform reduces latency and simplifies real-time ranking decisions.