RecSys: MF II

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Del curso dictado por Yandex

Big Data Applications: Machine Learning at Scale

26 calificaciones

Yandex

Big Data Applications: Machine Learning at Scale

26 calificaciones

Curso 3 de 5 en SpecializationBig Data for Data Engineers

Machine learning is transforming the world around us. To become successful, you’d better know what kinds of problems can be solved with machine learning, and how they can be solved. Don’t know where to start? The answer is one button away. During this course you will: - Identify practical problems which can be solved with machine learning - Build, tune and apply linear models with Spark MLLib - Understand methods of text processing - Fit decision trees and boost them with ensemble learning - Construct your own recommender system. As a practical assignment, you will - build and apply linear models for classification and regression tasks; - learn how to work with texts; - automatically construct decision trees and improve their performance with ensemble learning; - finally, you will build your own recommender system! With these skills, you will be able to tackle many practical machine learning tasks. We provide the tools, you choose the place of application to make this world of machines more intelligent. Special thanks to: - Prof. Mikhail Roytberg, APT dept., MIPT, who was the initial reviewer of the project, the supervisor and mentor of half of the BigData team. He was the one, who helped to get this show on the road. - Oleg Sukhoroslov (PhD, Senior Researcher at IITP RAS), who has been teaching MapReduce, Hadoop and friends since 2008. Now he is leading the infrastructure team. - Oleg Ivchenko (PhD student APT dept., MIPT), Pavel Akhtyamov (MSc. student at APT dept., MIPT) and Vladimir Kuznetsov (Assistant at P.G. Demidov Yaroslavl State University), superbrains who have developed and now maintain the infrastructure used for practical assignments in this course. - Asya Roitberg, Eugene Baulin, Marina Sudarikova. These people never sleep to babysit this course day and night, to make your learning experience productive, smooth and exciting.

De la lección

Recommender Systems

Collaborative Filtering RecSys: User-User and Item-Item10:58

RecSys: SVD I7:00

RecSys: SVD II8:10

RecSys: SVD III5:57

RecSys: MF I7:40

RecSys: MF II6:06

Conoce a los instructores

Vladimir Lesnichenko
Pavel Mezentsev
Senior Data Scientist
PulsePoint inc
Emeli Dral
Alexey A. Dral
Head of Big Data and Machine Learning
Algorithms and Programming Technologies dept. MIPT
Ilya Trofimov
Principal Data Scientist
Yandex
Evgeny Frolov
Data Scientist, PhD Student @Skoltech
Computational and Data Intensive Science and Engineering

Previously you have seen how to effectively deal with the missing values

within the basic matrix factorization approach.

Now, you can do something even more interesting, include user and

item biases into the model.

Let me briefly remind you what are those biases.

If for example a user has assigned rating 3 to some movie,

can you tell if the movie is actually not so good?

Well, if all other ratings provided by this user are higher, then probably yes.

But what if the user almost never rates movies with five stars, waiting for

something really exceptional?

Such a demanding user may have a different rating pattern,

shifted towards the lower ratings.

Similar intuition works for items, as well.

For example, popular items are likely to receive higher ratings.

You can conclude that the same rating value may contain different information,

depending on various biases.

In fact, most of the signal is contained within user and item biases, and

taking it into account may improve the quality of recommender systems.

One of the greatest benefits of the factorization approach

is the ability to flexibly modify the way utility function is defined,

while staying in the same computational framework.

As you remember, in the SVD model, you estimated LH values manually,

and then used them as a substitution for the unknowns.

But now, within your matrix factorization approach,

you can directly add all the needed biases to your utility function and

let your model figure out on its own what those biases actually are.

In other words, in this new model, you assume that using bias bi and

item biases, bj, are unknown.

And they become new model parameters along with factors p and q.

The global average mu is pre-computed in order to make the effect

of other biases more pronounced.

The new optimization objective becomes a bit more variables but

still very intuitive.

It has more parameters to estimate but

this doesn't change the general optimization procedure.

As in the previous case, you can use the approximate partial derivatives

to make computations more efficient,

employing the stochastic gradient descent to optimize their objective.

After differentiating with respect to different types of the parameters,

you obtain a new system of equations.

As you can see, the only difference is that two additional dumps related to user

and item biases are added.

Although this algorithm is not implemented in Spark out of the box,

there are a few open source-projects devoted to that.

There is also an efficient parallel implementation of the algorithm called

Hogwild.

This matrix factorization approach became popular after it was published

by Simon Funk when he attended the Netflix Prize competition.

Because of that, sometimes this algorithm is also called Funk SVD.

I hope you won't be confused by the name and can easily

explain the main differences between this approach and actual SVD method.

I also hope that you have enough confidence to make your own exploration of

the field of matrix factorization algorithms.

It might be good exercise to check your understanding, for example,

by looking into famous SVD++ and timeSVD models.

Now, as you have seen, there is one aspect common for

all previously discussed models.

All of them minimize the squared error.

In other words, these algorithms are designed to optimize RMSE metric,

which is suitable for the rating prediction task.

However, more often, you are interested not in the specific rating values, but

in a good list of top-N recommendations.

And for this task, the metrics like NDCG and MAP are more appropriate.

On the other hand, it turns out that the optimum value

in terms of an error-based metric like RMSE, doesn't necessarily correspond

to the optimum value in terms of the ranking-based metrics.

Of course, as you have previously seen,

it is possible simply to sort items by their predicted ratings.

However, this is not what you initially optimized for.

You can actually divide all those functions into three different groups.

The first one is called pointwise, and you are already familiar with it.

The second one is pairwise.

Can you spot the main difference of this loss function from the pointwise loss?

In the pairwise case, the function within summation

operates solely on this course predicted by the model,

while actual values are only used to enforce the ordering.

The main goal of the approach is to minimize the number of inversions when

the predicted order of any two items doesn't correspond to their natural order.

Finally, the listwise loss operates over the list or sets of items.

This one is especially suitable for

optimizing ranking metrics, such as MAP and NDCG.

Both pointwise and listwise approaches are members of

an important family of methods called learning to rank.

However, a bad optimization objective in terms of top-N recommendations task

comes at the cost of a high computational complexity.

Depending on the actual model, one may have to use additional computational fix

and simplifications in order to avoid exhaustive shorts and

deal with non-smooth nature of the metrics.

Which is why pointwise methods are still popular.

To summarize, you know how to extend basic matrix factorization and

include user and item biases into it.

You know about optimization objectives and

can explain the key differences between them.

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