rankm

RankM is a family of ranking methods used to integrate heterogeneous signals into a single relevance score. The central idea is to learn a scoring function that orders items in a way that matches observed preferences or outcomes. In typical formulations, each item i is described by a feature vector x_i, and a weight vector w is learned so that the score s(i) = f(w·x_i) preserves the order of preferred items. The function f is chosen to be monotone, ensuring that higher weighted scores yield higher ranks. RankM can be linear or incorporate non-linear transformations to capture complex interactions among features.

During training, rankM optimizes a loss over observed orderings or comparisons, often using pairwise or listwise objectives. Regularization terms promote generalization by limiting the model complexity. Some variants employ margin-based losses, probabilistic interpretations, or calibrated probability estimates, while others use differentiable surrogates to enable gradient-based optimization.

Applications span information retrieval, search result ranking, recommendation systems, and multi-criteria decision support. RankM is designed to handle heterogeneous data, such as textual signals, user behavior, and contextual metadata, by projecting them into a shared scoring space.

Limitations include sensitivity to data quality, reliance on representative training data, and potential bias in learned rankings. Evaluation commonly uses ranking metrics such as normalized discounted gain (NDCG) or precision at k.

See also: learning to rank, information retrieval, recommender systems.