Distancescore

Distancescore is a scalar value used to quantify how close two objects are within a given feature space. Unlike a direct distance, a distancescore typically maps a distance into a bounded or interpretable range, often emphasizing smaller distances with higher scores.

Common definitions: s(x, y) = f(d(x, y)) where d is a distance metric. Examples: s = 1 / (1 + d) and s = exp(-λ d). In practice, distancescores are computed for a pair or for a set of neighbors; for a set, s can be the average of inverse distances to k nearest neighbors.

Applications: distancescore is used in recommender systems to score candidate items by proximity to a user profile; in clustering to evaluate intra-cluster cohesion; in anomaly detection to flag points with unusually low distancescore to neighbors or normal data points.

Characteristics: distancescore depends on the chosen metric and the mapping function. It often requires normalization across datasets to be comparable and can be sensitive to scale, dimensionality, and the distribution of distances in the space.

Relation to other concepts: distancescore is related to similarity measures, proximity metrics, and kernel-based scores, but it is not a standardized term on its own. It is often described descriptively in domain-specific contexts to convey proximity in a convenient score form.

Implementation notes: select an appropriate distance metric for the data (e.g., Euclidean, Manhattan, cosine). Choose a mapping function that yields a meaningful range and interpretability, and document the intended meaning of the score for users and stakeholders.