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Sep 25th 2017

Recommender Systems: Evaluation and Metrics (Coursera)

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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.


Course 3 of 5 in the Recommender Systems Specialization.


Syllabus


WEEK 1

Preface

Basic Prediction and Recommendation Metrics

Graded: Basic Prediction and Recommendation Metrics Assignment


WEEK 2

Advanced Metrics and Offline Evaluation (Part 1)

Graded: Offline Evaluation and Metrics Quiz


WEEK 3

Online Evaluation

Graded: Online Evaluation Quiz


WEEK 4

Evaluation Design

Graded: Assignment: Evaluation Design Cases


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