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.
推荐系统 专项课程
Master recommender systems.. Learn to design, build, and evaluate recommender systems for commerce and content.
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关于此 专项课程
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您将学到的内容有
Build recommendation systems
Implement collaborative filtering
Master spreadsheet based tools
Use project-association recommenders
您将获得的技能
Collaborative FilteringRecommender SystemsEvaluationLensKitMatrix Factorization
100% 在线课程
立即开始，按照自己的计划学习。
灵活的计划
设置并保持灵活的截止日期。
中级
完成时间大约为2 个月
建议 9 小时/周
英语（English）
字幕：英语（English）
专项课程的运作方式
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在结束每门课程并完成实践项目之后，您会获得一个证书，您可以向您的潜在雇主展示该证书并在您的职业社交网络中分享。
此专项课程包含 5 门课程
Introduction to Recommender Systems: Non-Personalized and Content-Based
487 个评分
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.
Nearest Neighbor Collaborative Filtering
243 个评分
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.
Recommender Systems: Evaluation and Metrics
176 个评分
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.
Matrix Factorization and Advanced Techniques
145 个评分
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.
关于 明尼苏达大学
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.
审阅
4.2
来自推荐系统的热门评论
由 PS 提供Dec 10, 2016
As a software engineer with computer science background I found that course enhancing my knowledge. I'm going to continue the specialization.
由 DR 提供Oct 8, 2016
More information on Programming Assignment would have been helpful . Overall a good course to begin the specialization
由 NS 提供Dec 11, 2019
i found this course very helpful and informative. it explains the theory while providing real-world examples on recommender systems. the assignment helps in clearing up any confusion with the material
由 SS 提供Mar 30, 2019
Thank you so very much to open my eye see more view of recommendation field not only algorithms but use case and many trouble-shooting in worldwide business, moreover interview with noble professor.
由 DG 提供Feb 1, 2020
I found this course very informative and clears lot of concept in Item based and used based collaborative filtering. Spreadsheet assignment helped me to clearly understand the algorithms.
由 BS 提供Feb 12, 2019
One of the best courses I have taken on Coursera. Choosing Java for the lab exercises makes them inaccessible for many data scientists. Consider providing a Python version.
由 DP 提供Dec 7, 2017
Nice introduction to recommender systems for those who have never heard about it before. No complex mathematical formula (which can also be seen by some as a downside).
由 IP 提供Sep 18, 2016
it's a fantastic course that gives you a good idea of what the objectives of recommender systems are and some intuition on the way how it can be accomplished.
常见问题
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可以！点击您感兴趣的课程卡开始注册即可。注册并完成课程后，您可以获得可共享的证书，或者您也可以旁听该课程免费查看课程资料。如果您订阅的课程是某专项课程的一部分，系统会自动为您订阅完整的专项课程。访问您的学生面板，跟踪您的进度。
有助学金吗？
我可以免费学习课程吗？
此课程是 100% 在线学习吗？是否需要现场参加课程？
此课程完全在线学习，无需到教室现场上课。您可以通过网络或移动设备随时随地访问课程视频、阅读材料和作业。
完成专项课程需要多长时间？
Most learners should be able to complete the specialization in 20-26 weeks.
What background knowledge is necessary?
Basic statistics or college algebra, and an ability to work with spreadsheets. For the honors track, you should also be comfortable implementing software in Java.
Do I need to take the courses in a specific order?
While each component can be useful by itself, the courses do build on each other and should be taken in order.
完成专项课程后我会获得大学学分吗？
The University of Minnesota does not offer credit for completing this specialization. If you are enrolled elsewhere, you may wish to speak with your advisor or program staff to find out whether this specialization could be used for independent study credit.
What will I be able to do upon completing the Specialization?
You will understand and be able to apply the major families of recommender algorithms: non-personalized, product association, content-based, nearest-neighbor, and matrix factorization. You will know and be able to apply a variety of recommender metrics, and will be able to use this knowledge to match the correct recommender system to appplications.
What is the honors track?
The honors track is an optional track where learners add programming recommenders in the open source LensKit toolkit. You should be comfortable with basic data structures, algorithms, and Java to attempt the honors track.
How does this Specialization relate to the prior Recommender Systems courses?
This specialization is an extended and updated version of the two prior versions of Introduction to Recommender Systems that we've offered through Coursera. About 50% of the video and 80% of the assessment material are new, and there is an honors track with programming assignments (which existed in the first version of the course only, and have been re-done for this specialization). The Capstone is entirely new.
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