Transrating

Transrating is a methodological process used to translate ratings from one scale, rating system, or domain into another, with the goal of preserving the relative information and enabling cross-context comparison. It is applied in consumer research, cross-platform analytics, meta-analyses, and recommender systems where data originate from different scales such as 1–5 stars, 0–100 points, or categorical labels.

Techniques include:

- Linear scale mapping: rescale a value using known minimum and maximum values to a target range, often with a formula such as new = (old - old_min) / (old_max - old_min) * (new_max - new_min) + new_min.

- Nonlinear transformations: apply functions (e.g., logarithmic, piecewise) to account for perceptual or distributional differences between scales.

- Rank-based normalization: convert values to percentiles or z-scores to preserve relative ordering.

- Anchor-based calibration: use common reference items observed on multiple scales to align scales.

- Model-based calibration: fit regression or Bayesian models to predict target-scale values from source-scale values.

Applications include enabling fair cross-platform comparisons of product ratings, harmonizing data across studies, improving robustness of meta-analyses, and informing recommender-system cross-training.

Limitations and considerations: transrating can introduce distortions if scales differ in underlying distributions, due to ceiling or floor effects, cultural or contextual biases, time drift, or divergent constructs. Validation with held-out data and domain knowledge is recommended.

See also: normalization, calibration, scaling, meta-analysis, cross-platform analytics.