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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/recommender-metrics
课程评论：没有评论
课程名称：推荐系统：评估与指标 概述：在本课程中，您将学习如何评估推荐系统。您将熟悉几类指标，包括测量预测准确性、排名准确性、决策支持，以及多样性、产品覆盖率和意外性等其他因素。您将了解不同指标如何与用户目标和商业目标相关联。此外，您还将学习如何严格地进行离线评估（即如何准备和抽样数据，如何汇总结果），以及在线（实验）评估。完成课程后，您将掌握比较不同推荐系统选择所需的工具，适用于各种用途。 课程大纲： 第一部分：基本预测和推荐指标 描述：该部分介绍推荐系统中基础的预测和推荐指标。 第二部分：高级指标与离线评估 描述：该部分深入探讨高级指标，以及如何开展离线评估。 第三部分：在线评估 描述：在该部分中，您将学习如何进行在线评估。 第四部分：评估设计 描述：该部分重点讲解评估的设计过程。
Part: 1
Title:Basic Prediction and Recommendation Metrics
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Part: 2
Title:Advanced Metrics and Offline Evaluation
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Part: 3
Title:Online Evaluation
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Part: 4
Title:Evaluation Design
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In this course you will learn how to evaluate recommender systems. You will gain familiarity with several families of metrics, including ones to measure prediction accuracy, rank accuracy, decision-support, and other factors such as diversity, product coverage, and serendipity. You will learn how different metrics relate to different user goals and business goals. You will also learn how to rigorously conduct offline evaluations (i.e., how to prepare and sample data, and how to aggregate results). And you will learn about online (experimental) evaluation. At the completion of this course you will have the tools you need to compare different recommender system alternatives for a wide variety of uses.
