Modern e-commerce product discovery fails when it processes relevance signals in separate pipelines. Tensor-based ranking models address this by evaluating semantic embeddings, behavioral signals, structured product attributes, and business context simultaneously within a single multidimensional structure. Unlike vectors (1D),
Table of contents
What is tensor-based ranking?The limits of traditional rankingVectors vs. tensors in product discoveryWhy tensor support needs to be built into the search platformEvaluating product discovery platforms for tensor-based rankingLooking AheadSort: