Recommender Systems 专项课程

于 3月 27 开始

Recommender Systems 专项课程

Master Recommender Systems

Learn to design, build, and evaluate recommender systems for commerce and content.

本专项课程介绍

This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative techniques. Designed to serve both the data mining expert and the data literate marketing professional, the courses offer interactive, spreadsheet-based exercises to master different algorithms along with an honors track where learners can go into greater depth using the LensKit open source toolkit. A Capstone Project brings together the course material with a realistic recommender design and analysis project.

制作方:

courses
5 courses

按照建议的顺序或选择您自己的顺序。

projects
项目

旨在帮助您实践和应用所学到的技能。

certificates
证书

在您的简历和领英中展示您的新技能。

课程
Intermediate Specialization.
Some related experience required.
  1. 第 1 门课程

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

    即将开课的班次:3月 27 — 5月 1。
    课程学习时间
    4 weeks; an average of 3-7 hours per week, plus 2-5 hours per week for honors track.
    字幕
    English

    课程概述

    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. After completing this course, you will be able to compute a variety of recommendations from datasets using basic spreadsheet tools, and if you complete the honors track you will also have programmed these recommendations using the open source LensKit recommender toolkit. In addition to detailed lectures and interactive exercises, this course features interviews with several leaders in research and practice on advanced topics and current directions in recommender systems.
  2. 第 2 门课程

    Nearest Neighbor Collaborative Filtering

    即将开课的班次:4月 3 — 5月 8。
    字幕
    English

    课程概述

    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.
  3. 第 3 门课程

    Recommender Systems: Evaluation and Metrics

    即将开课的班次:3月 27 — 5月 1。
    字幕
    English

    课程概述

    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.
  4. 第 4 门课程

    Matrix Factorization and Advanced Techniques

    于 March 2017 开始
    字幕
    English

    课程概述

    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.
  5. 第 5 门课程

    Recommender Systems Capstone

    于 April 2017 开始
    字幕
    English

    毕业项目介绍

    This capstone project course for the Recommender Systems Specialization brings together everything you've learned about recommender systems algorithms and evaluation into a comprehensive recommender analysis and design project. You will be given a case study to complete where you have to select and justify the design of a recommender system through analysis of recommender goals and algorithm performance. Learners in the honors track will focus on experimental evaluation of the algorithms against medium sized datasets. The standard track will include a mix of provided results and spreadsheet exploration. Both groups will produce a capstone report documenting the analysis, the selected solution, and the justification for that solution.

制作方

  • 明尼苏达大学

    The University of Minnesota has been a leader in recommender systems since developing GroupLens, the first automated recommender system in 1993. Today the University continues that leadership with leading research on recommender algorithms, applications, and evaluation.

    The University of Minnesota is among the largest public research universities in the country, offering undergraduate, graduate, and professional students a multitude of opportunities for study and research. Located at the heart of one of the nation’s most vibrant, diverse metropolitan communities, students on the campuses in Minneapolis and St. Paul benefit from extensive partnerships with world-renowned health centers, international corporations, government agencies, and arts, nonprofit, and public service organizations.

  • Joseph A Konstan

    Joseph A Konstan

    Distinguished McKnight Professor and Distinguished University Teaching Professor
  • Michael D. Ekstrand

    Michael D. Ekstrand

    Assistant Professor

FAQs

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