Knowledge about Ranking Algorithms

最新推荐文章于 2024-10-19 17:43:51 发布

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本文探讨了当前排名算法的三大类别：点对点、配对和列表级算法。点对点算法设计评分函数独立评估文档；配对算法最小化文档间的相对误分类误差；列表级算法通过最小化列表损失函数来学习排名模型。

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1. Type

Most current ranking algorithms can be divided into three main categories: pointwise, pairwise and listwise.

Pointwise ranking algorithms
This type of algorithms does not use any relative information between documents, instead attempting to design a score function, scores of which are then used to rank the documents.

Examples: PRank

Pairwise ranking algorithms
The goal of these algorithms is to minimize the relative pairwise misclassification error in ranking problem.

Examples: RankNet, LambdaRank, RankBoost

Listwise ranking algorithms
These algorithms use lists of ranked documents as “instances” during training, and learn a ranking model by minimizing some listwise loss function.

Examples: AdaRank, SoftRank, ListNet, C-CRF

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