Recommender Systems Specialization

开始时间: 06/21/2022 持续时间: Approximately 5 months to complete Suggested pace of 3 hours/week

所在平台: Coursera专项课程

课程类别: 计算机科学

大学或机构: CourseraNew



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A Recommender System is a process that seeks to predict user preferences. This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative filtering techniques, as well as advanced topics like matrix factorization, hybrid machine learning methods for recommender systems, and dimension reduction techniques for the user-product preference space. This Specialization is designed to serve both the data mining expert who would want to implement techniques like collaborative filtering in their job, as well as the data literate marketing professional, who would want to gain more familiarity with these topics. The courses offer interactive, spreadsheet-based exercises to master different algorithms, along with an honors track where you can go into greater depth using the LensKit open source toolkit. By the end of this Specialization, you’ll be able to implement as well as evaluate recommender systems. The Capstone Project brings together the course material with a realistic recommender design and analysis project.

推荐人系统专业化:推荐人系统是一个旨在预测用户偏好的过程。本专业涵盖了推荐系统中的所有基本技术,从非个性化和项目关联推荐器到基于内容的协作过滤技术,以及高级主题,例如矩阵分解,推荐系统的混合机器学习方法和降维技术用户产品偏好空间。 该专长旨在为希望在工作中实施协作过滤等技术的数据挖掘专家以及希望对这些主题更加熟悉的数据知识型营销专家提供服务。 这些课程提供基于电子表格的交互式练习,以掌握不同的算法,并提供荣誉轨道,您可以在这里使用LensKit开源工具包进行更深入的研究。 在本专业课程结束时,您将能够实施和评估推荐系统。顶峰项目将课程资料与现实的推荐者设计和分析项目结合在一起。


Course: 1

Course Link:

Title:Introduction to Recommender Systems: Non-Personalized and Content-Based

Description:This course, which is designed to serve as the first course in the Recommender Systems specialization, introduces the concept of recommender systems, reviews several examples in detail, and leads you through non-personalized recommendation using summary statistics and product associations, basic stereotype-based or demographic recommendations, and content-based filtering recommendations.

Course: 2

Course Link:

Title:Nearest Neighbor Collaborative Filtering

Description:In this course, you will learn the fundamental techniques for making personalized recommendations through nearest-neighbor techniques. First you will learn user-user collaborative filtering, an algorithm that identifies other people with similar tastes to a target user and combines their ratings to make recommendations for that user. You will explore and implement variations of the user-user algorithm, and will explore the benefits and drawbacks of the general approach. Then you will learn the widely-practiced item-item collaborative filtering algorithm, which identifies global product associations from user ratings, but uses these product associations to provide personalized recommendations based on a user's own product ratings.

Course: 3

Course Link:

Title:Recommender Systems: Evaluation and Metrics

Description: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: 4

Course Link:

Title:Matrix Factorization and Advanced Techniques

Description:In this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building recommender systems based on reducing the dimensionality of the user-product preference space. Then you will learn about techniques that combine the strengths of different algorithms into powerful hybrid recommenders.



明尼苏达大学的推荐系统专项课程系列(Recommender Systems Specialization),这个系列由4门子课程和1门毕业项目课程组成,包括推荐系统导论,最近邻协同过滤,推荐系统评价,矩阵分解和高级技术等,感兴趣的同学可以关注:Master Recommender Systems-Learn to design, build, and evaluate recommender systems for commerce and content.


推荐系统 推荐系统课程 推荐系统导论 最近邻协同过滤 推荐系统评价 矩阵分解 协同过滤