LORE framework jointly learns dimensionality and similarity from ordinal data

Researchers introduce LORE (Low Rank Ordinal Embedding), a scalable method for recovering the structure of subjective perceptual spaces such as taste, smell, and aesthetics. Unlike prior approaches, it estimates the intrinsic dimensionality and the relative similarity relationships of items at the same time from ordinal comparisons. The work targets settings where only ranked or comparative judgments, rather than absolute numeric ratings, are available.