resultrelated

Resultrelated is a term used in data analysis, information retrieval, and machine learning to denote features, metrics, or signals that describe how a given result relates to other results, contexts, or outcomes. It captures the idea that an item should not be evaluated in isolation but in connection with surrounding results, user behavior, or query context. The term often appears in discussions of ranking, diversification, and context-aware recommendations.

In practice, resultrelated features can include measures of relevance to neighboring results, redundancy or novelty relative to a result list, co-occurrence patterns in user interactions, or contextual similarity to the user’s query and session history. For example, in a search system, resultrelated signals might help balance relevance with diversity by reducing redundancy among top results or by surfacing items that complement what a user has already seen. In recommender systems, resultrelated features can support reranking or diversification to reflect relationships among recommended items.

Measurement and modeling of resultrelated signals typically rely on similarity metrics, correlation or mutual information, and embedding-based representations that quantify how closely a result is connected to others. They may be learned as part of a ranking model or used as auxiliary features to improve contextual relevance and user satisfaction.

See also: diversity, co-occurrence, similarity, feature engineering, contextual features, recommender systems.