Rankaware
Rankaware is a term used in information retrieval and machine learning to describe systems that are explicitly aware of the ranking signals that determine the order of items in a list. Rank-aware design aims to align model behavior with the significance of item positions, rather than treating ranking as a mere byproduct of prediction.
In practice, rank-awareness can be realized through data representations, learning objectives, and evaluation methods that incorporate
Common metrics associated with rank-aware evaluation include normalized discounted cumulative gain (NDCG), mean reciprocal rank (MRR),
Applications span search engines, recommendation engines, and ad placement, where rank-aware models can help present users
Challenges include bias and fairness considerations in ranking, interpretability of why certain items are ranked highly,
See also: learning to rank, ranking metrics, listwise loss, fairness in information retrieval.