Recommender systems have changed the way people find products, information, and even other people. They study patterns of behavior to know what someone will prefer from among a collection of things he has never experienced. The technology behind recommender systems has evolved over the past 20 years into a rich collection of tools that enable the practitioner or researcher to develop effective recommenders. We will study the most important of those tools, including how they work, how to use them, how to evaluate them, and their strengths and weaknesses in practice.
The algorithms we will study include content-based filtering, user-user collaborative filtering, item-item collaborative filtering, dimensionality reduction, and interactive critique-based recommenders. The approach will be hands-on, with six week projects, each of which will involve implementation and evaluation of some type of recommender.
In addition to topical lectures, this course includes interviews and guest lectures with experts from both academia and industry.
Beginning in February 2015, you will be able to earn a Verified Certificate by verifying your identity via a webcam and a government-issued ID. This option will provide formal recognition of your achievements in the course and includes the University of Minnesota logo. Before then, you can complete a “test run” of the exam. You can then re-take the exam after the Verified Certificate becomes available. For information regarding Verified Certificates, see https://courserahelp.zendesk.com/hc/en-us/articles/201212399-Verified-Certificates
Introduction to Recommender Systems This module introduces recommender systems and the course. It includes a detailed taxonomy of the types of recommender systems, and also includes a detailed tour of Amazon.com’s recommenders. There is an introductory assessment in the final lesson that leads you through exploring recommender systems on their own.
Non-Personalized Recommenders This module covers non-personalized recommender systems, including recommendation based on summary statistics and on product-association rules. These recommenders, which are widely used in practice, include overall popularity (how many people like this? what’s the average rating?) and product-to-product recommenders such as “people who bought this item also bought” recommenders. There is an assessment at the end of the module that has you compute non-personalized recommendations.
Content-Based Recommenders This module covers content-based recommender systems. These systems build a profile of content preferences based on the content attributes associated with items the users has liked or disliked. We’ll discuss common mechanisms for building and maintaining content preference profiles and have an assessment that has you complete hand computations of content profiles and recommendations.
User-User Collaborative Filtering This module covers user-user collaborative filtering recommender systems. This classic method matches a user against other users with similar preferences and then combines the preferences of those “nearest neighbor” users to form predictions and recommendations. We cover a number of tunings and variations on the algorithm, and have an assessment where you implement your own user-user CF recommender in a spreadsheet.
Evaluation This module focuses on metrics and evaluation. It introduces a variety of metric types, individual metrics, experimental techniques, and evaluation goals. In many ways, it is at the heart of the course -- what’s the point in having lots of different algorithms if you can’t tell which is better in a situation? The assessment at the end of this module takes you through a set of situations to test your understanding of effective evaluation.
Item Based This module introduces item-item collaborative filtering, an early innovation that improved run-time performance by computing relationships among items from user rating data. We also look at the interesting case of unary implicit data (like it or don’t know) and have an assessment that has you compute item-item recommendations in a spreadsheet.
Dimensionality Reduction This module introduces matrix factorization recommendation algorithms, the class of algorithms that seems to be among the most promising today for good recommendation quality and scalability. We introduce you to the concepts behind these algorithms, some specific implementations, and a look at current directions. Your last assessment in the course involves computing predictions and recommendations from factored-matrix representations of ratings matrices.
Advanced Topics This is our concluding module; it includes coverage of topics such as security threats and the cold-start problem as well as a number of other practical issues. This module also consists of a three-part final exam, covering modules 6-8.
completed this course, spending 4 hours a week on it and found the course difficulty to be very easy.
This is probably still the best introduction to recommender systems available, better than some of the textbooks that have been written on the topic. It does an excellent job of covering the basic topics and providing pointers for further study. However in its current on-demand form it suffers from a...
This is probably still the best introduction to recommender systems available, better than some of the textbooks that have been written on the topic. It does an excellent job of covering the basic topics and providing pointers for further study. However in its current on-demand form it suffers from a number of problems in execution. 1. Numerous errors, in both the technical content and the assignments. 2. The on-demand format is difficult to navigate (e.g., you cannot download the lecture slides) 3. The programming assignments do not make use of the coursera unit-testing grader and simply ask you to manually fill in the recommender results, this is both time consuming and non-informative when you get it wrong 4. Programming exercises have been reformulated to be doable in Excel without relying on R or Matlab (I imagine 99% of serious students still end up using R, Matlab or Python regardless, so not sure what their goal is here), so they are incredibly simple. There is no sense of the relative improvements/tradeoffs of each method implemented, since there is no additional code to evaluate them. 5. Extremely long lectures with low information density. The amount of technical coverage is low, and the nontechnical sections are so long that I wish I had a x4 playback speed option or could just read the transcript and move on.
completed this course, spending 10 hours a week on it and found the course difficulty to be hard.
An interesting, but a very massive course about Recommender Systems. Lections are very long, and even hough there is no "low information density", as was said before (I totally disagree wih this statement), part of the information could be throught out, regarding the introductionary level of the course.
Being a non-english speaker, I had to pass a long-long hours, lisening to the course, and this fact (and not the course's content) that made it "Hard". I would better call It "harrasing".
I took this course in order to have ability to choose the recommender structure for my site, that someone other is building, so I was not interesting in programming part (that is out in on-demand version). Such a way, I reached my goal by 100%. If I should build It by my-self, this course would be absolutely unsufficient.
I really like the course but lectures are too long as they could be shortened to learn the same. There's a little too much simple talk and not as much tech-y as it could be.
In Machine Learning course at Coursera I really like that the professor writes down the formulas and explains stuff by writing so you're following him more than when stuff is just written on the presentation.
But all-in-all, very informative and I'm still going to learn a lot. Very good introduction!
Glosses over technical concepts at a high-level without going into detail. A lot of demonstrations from real-life applications of where and why these concepts are applied, but no actual examples of HOW the concepts inside actually work such as Content-Based and Collaborative Filtering in the first introduction course.
There's no summary or slides of each of the lecture videos so if you forget or miss out on any material, you'll have to browse through the videos or the transcript over to find the content you're looking for.
The honours track assignment uses the LensKit, Gradle, and other libraries that new users with no experience find confusing.
lectures are too long, but the information density is low. It waste time, and so boring when watching these lectures. The second drawback is that using excel is not really a good tools to solve assignments, because R or Python is a better choice.