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Decay Ranker

この章では、Decay Ranker（減衰ランカー）について説明します。Decay Ranker は動的ランキング手法を用いて、特定の数値フィールドの値に応じて特定エンティティの類似度スコアを減衰させることで、他のエンティティを目立たせます。

Gaussian Decay [READ MORE]

Gaussian decay, also known as normal decay, creates the most natural-feeling adjustment to your search results. Like human vision that gradually blurs with distance, Gaussian decay creates a smooth, bell-shaped curve that gently reduces relevance as items move away from your ideal point. This approach is ideal when you want a balanced decay that doesn't harshly penalize items just outside your preferred range but still significantly reduces the relevance of distant items.

Exponential Decay [READ MORE]

Exponential decay creates a steep initial drop followed by a long tail in your search results. Like a breaking news cycle where relevance diminishes rapidly at first but some stories retain importance over time, exponential decay applies a sharp penalty to items just beyond your ideal range while still keeping distant items discoverable. This approach is ideal when you want to heavily prioritize proximity or recency but don't want to completely eliminate more distant options.

Linear Decay [READ MORE]

Linear decay creates a straight-line decline that terminates at an absolute zero point in your search results. Like an upcoming event countdown where relevance gradually fades until the event has passed, linear decay applies a predictable, steady reduction in relevance as items move away from your ideal point until they completely disappear. This approach is ideal when you want a consistent decay rate with a clear cutoff, ensuring that items beyond a certain boundary are completely excluded from results.

チュートリアル：時間ベースのランキングの実装 [READ MORE]

多くの検索アプリケーションにおいて、コンテンツの新しさは関連性と同じくらい重要です。ニュース記事、商品リスト、ソーシャルメディアの投稿、研究論文などはすべて、意味的な関連性と新しさをバランスよく考慮したランキングシステムから恩恵を受けます。このチュートリアルでは、減衰ランカーを使用して Zilliz Cloud で時間ベースのランキングを実装する方法を示します。