Relevantsi

Relevantsi is a proposed metric used to quantify how relevant a piece of content is to a defined topic, user intent, or context. The concept is used in information retrieval, recommendation systems, and analytics to prioritize items that better match needs or queries.

Definition and scope: Relevantsi integrates semantic similarity, topical coverage, recency, authority, and personalization signals. Values are typically normalized on a 0 to 1 scale, or 0 to 100, with higher scores indicating greater relevance.

Calculation: In practice, relevantsi may be computed as a weighted sum: Relevantsi = w1*semantic + w2*topic + w3*recency + w4*authority + w5*personalization, where weights reflect application goals. Semantic is derived from vector embeddings; topic from topic models; recency from timestamp; authority from source credibility; personalization from user model.

Applications: sequential ranking in search results, feed ranking, document filtering, and anomaly detection for content that deviates from context.

Limitations and standardization: There is no universal standard definition or measurement; implementations vary across teams. Challenges include biases in data, ambiguity in user intent, and dynamic contexts.

See also: relevance, information retrieval, cosine similarity, vector space model.