HypRQ-VAE Learns Item Indexing in Hyperbolic Space for Long-Tail-Aware Generative Recommendation
_reachsumit · x · 2026-09-04
HypRQ-VAE introduces the first framework to learn item indexing in hyperbolic space for generative recommender systems, addressing hallucinations caused by the mismatch between LLM text tokens and discrete item indices.
- Existing Euclidean-space item vocabularies struggle with real catalogs' long-tail power-law distributions
- Hyperbolic geometry's exponential volume growth naturally accommodates user-item interaction power laws, preserving both textual semantics and representation fidelity for sparse tail items
- Built on a Hyperbolic Residual-Quantized Variational AutoEncoder
- Shows significant improvements on three benchmark datasets
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