𝗜𝗻 𝗲-𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗲 𝗮𝗻𝗱 𝗻𝗲𝘄𝘀 𝘀𝗲𝗮𝗿𝗰𝗵, 𝘀𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝘀𝗶𝗺𝗶𝗹𝗮𝗿𝗶𝘁𝘆 ...

Milvus on X: "𝗜𝗻 𝗲-𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗲 𝗮𝗻𝗱 𝗻𝗲𝘄𝘀 𝘀𝗲𝗮𝗿𝗰𝗵, 𝘀𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝘀𝗶𝗺𝗶𝗹𝗮𝗿𝗶𝘁𝘆 𝗮𝗹𝗼𝗻𝗲 𝗶𝘀 𝗻𝗼𝘁 𝗲𝗻𝗼𝘂𝗴𝗵 𝘁𝗼 𝗿𝗮𝗻𝗸 𝗿𝗲𝘀𝘂𝗹𝘁𝘀. Milvus's Time-aware Ranking Functions (also called Decay Functions) rerank retrieval results by applying time-decay https://t.co/NK0BLx23nP" / X

Milvus

@milvusio

𝗜𝗻 𝗲-𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗲 𝗮𝗻𝗱 𝗻𝗲𝘄𝘀 𝘀𝗲𝗮𝗿𝗰𝗵, 𝘀𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝘀𝗶𝗺𝗶𝗹𝗮𝗿𝗶𝘁𝘆 𝗮𝗹𝗼𝗻𝗲 𝗶𝘀 𝗻𝗼𝘁 𝗲𝗻𝗼𝘂𝗴𝗵 𝘁𝗼 𝗿𝗮𝗻𝗸 𝗿𝗲𝘀𝘂𝗹𝘁𝘀. Milvus's Time-aware Ranking Functions (also called Decay Functions) rerank retrieval results by applying time-decay functions, so recent content ranks higher without sacrificing relevance. Milvus's Decay Functions combining vector similarity with configurable time-decay to dynamically adjust each document's relevance score. 𝗧𝗵𝗲 𝘀𝗰𝗼𝗿𝗶𝗻𝗴 𝘄𝗼𝗿𝗸𝘀 𝗶𝗻 𝘁𝗵𝗿𝗲𝗲 𝘀𝘁𝗲𝗽𝘀: • 𝗖𝗼𝗻𝘃𝗲𝗿𝘁 𝗱𝗶𝘀𝘁𝗮𝗻𝗰𝗲𝘀 𝗶𝗻𝘁𝗼 𝗵𝗶𝗴𝗵𝗲𝗿-𝗶𝘀-𝗯𝗲𝘁𝘁𝗲𝗿 𝘀𝗰𝗼𝗿𝗲𝘀. For L2 and JACCARD (lower = more similar): normalized_score = 1.0 − (2 × arctan(score)) / π. COSINE, IP, and BM25 scores are used directly. • 𝗖𝗼𝗺𝗽𝘂𝘁𝗲 𝗮 𝗱𝗲𝗰𝗮𝘆 𝘀𝗰𝗼𝗿𝗲 from a numeric field like a timestamp, using one of three decay models: exponential, Gaussian, or linear. The result is a value between 0 and 1, representing how close the document is to a reference point (typically "now"). • 𝗠𝘂𝗹𝘁𝗶𝗽𝗹𝘆: final_score = normalized_similarity_score × decay_score. In hybrid search with multiple vector fields, Milvus takes the maximum normalized score first: final_score = max(normalized_scores) × decay_score. Each model takes a configurable origin, scale (the distance beyond the offset at which the score drops to the decay value), and an optional offset (a no-penalty zone around the origin where the score stays at 𝟭.𝟬). 𝗟𝗲𝗮𝗿𝗻 𝗺𝗼𝗿𝗲 𝗵𝗲𝗿𝗲:

milvus.io/docs/decay-ran…

3:00 PM · Jul 16, 2026

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