Dragging Hyperpersonalization Into the Modern Era: Tensor Modes, GenAI Embeddings, and Joint Semantic Spaces

Abstract

Hyperpersonalization—serving relevant recommenda- tions in rare conditional slices—remains resistant to mod- ern deep learning. Transformers dominate head-heavy metrics but fail when user-item-context-time combinations are sparse. We argue this failure is architectural: feature- collapse destroys the conditional structure that hyperper- sonalization requires. We introduce tensor decomposition and joint semantic embeddings as additive representational primitives—structure that expands what downstream learners can express with- out constraining model choice. Three components integrate into a coherent framework: (1) explicit tensormodes for each business axis; (2) semantic coupling to GenAI embeddings via sparsity-adaptive regularization; and (3) joint embed- ding spaces that scale to N modalities without dimension explosion. Sequential models, graph methods, and policy learners compose on top without modification. A hybrid transformer-tensor architecture achieves +74% tail recall lift over matrix factorization on the H&M Kaggle dataset. The learned fusion weight ( α = 0.60) reveals complementary signal extraction that neither modality achieves alone. In production, tensor scoring serves as a second-stage reranker on ANN-retrieved candidates, with frozen factors enabling low-latency inference. We situate this work within industry deployments at Netflix, Alibaba, Yahoo, and Capital One, and discuss applications across retail, financial services, media, and healthcare—where tail queries represent customer lifetime value, not leaderboard noise. The architecture exists. The ablations are clean. Any enterprise serious about hyperper- sonalization should validate this on their own data.