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When we think about a ratings matrix and we think about having millions or
hundreds of millions of users and thousands, or hundreds of thousands, or
millions of products, what we have is a highly specialized representation.
And frankly an overly detailed representation of taste.
What do people like?
And what do they like in products?
The whole idea of the techniques were studying in this course is to
find a way to compress that representation and distill out whether there
were some essential and important dimensions of taste.
That if we can represent, we can simplify the whole process of
recommendation into identifying a person's taste and
identifying how each individual product matches against taste space.
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Now this gives us the promise of efficiency and higher quality.
This is one of those cases were compressing and
losing a little bit of data, helps us get over artifacts and
get to a smoother and more realistic rendering of people's taste.
So, in this course,
we're going to be looking at exploiting the best of machine learning.
Machine learning techniques have been doing a lot to look at dimensionality
reduction and at how we can, not only take a look at taste as a function of ratings,
but take the both end approach of weaving in whatever we know about
user properties like demographics, item properties like content,
even contextual properties, all into a model that represents taste
as compactly as possible, to give us the most effective recommendations possible.
>> The key concepts of this course start with matrix factorization for
collaborative filtering, where we'll learn how to break the ratings
matrix down into smaller matrices that describe user preference for
different types of items or characteristics of items, and
the extent to which items express those characteristics.
Through this, we'll also start to look at machine learning and
optimization approaches to recommender systems, where we directly learn our
recommender that minimizes some error or maximizes some kind of utility function.
We'll then move in to hybrid algorithms which look at combining
multiple algorithms into one, so you could combine a collaborative filter and
a content based filter for example.
To allow their strength to compliment each other and
build a better recommendation than either one can compute alone.
These types of recommenders are very widely used in practice.
We'll then look at learning-to-rank which looks at what happens if we skip
this whole prediction thing entirely?
And directly optimize our recommender for producing good top end lists.
And finally we will talk about number of advanced topics such as context to where
recommendation, aspects, and issues of deploying recommenders at scale on
industrial applications and several addition topics.
>> This course is structured around three major topics.
Weeks one and two focus on matrix factorization.
There will be a spreadsheet assignment that builds upon matrix factorization.
And for the honors track a programming assignment plus a quiz.
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Week three will focus on hybrid algorithms,
which will also have a spreadsheet assignment and a quiz.
And week four will look at advanced techniques and topics,
where there will be an honors assignment programmed in LensKit as well as
the final quiz of this course and of this specialization.
Let's move forward into matrix factorization.
Michael D. EkstrandAssistant Professor
Joseph A KonstanDistinguished McKnight Professor and Distinguished University Teaching Professor
