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Lecture 5 Instructor: Max Welling Squared Error Matrix Factorization total of +/- 400,000,000 nonzero entries (99% sparse) movies (+/- 17,770) users (+/- 240,000) Remember User Ratings We are going to “squeeze out” the noisy, un-informative information from the matrix.

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total of +/- 400,000,000 nonzero entries

(99% sparse)

movies (+/- 17,770)

users (+/- 240,000)

Remember User Ratings- We are going to “squeeze out” the noisy, un-informative information from
- the matrix.
- In doing so, the algorithm will learn to only retain the most valuable information
- for predicting the ratings in the data matrix.
- This can then be used to more reliably predict the entries that were not rated.

x

movies (+/- 17,770)

Squeezing out Informationtotal of +/- 400,000,000 nonzero entries

(99% sparse)

movies (+/- 17,770)

users (+/- 240,000)

K

users (+/- 240,000)

“K” is the number of factors, or topics.

Interpretation

star-wars = [10,3,-4,-1,0.01]

K

- Before, each movie was characterized by a signature
- over 240,000 user-ratings.
- Now, each movie is represented by a signature of
- K “movie-genre” values (or topics, or factors)
- Movies that are similar are expected to have similar
- values for their movie genres.

movies (+/- 17,770)

users (+/- 240,000)

K

- Before, users were characterized by their ratings over all movies.
- Now, each user is represented by his/her values over movie-genres.
- Users that are similar have similar values for their movie-genres.

Dual Interpretation

- Interestingly, we can also interpret the factors/topics as
- user-communities.
- Each user is represented by its “participation” in K
- communities.
- Each movie is represented by the ratings of these
- communities for the movie.
- Pick what you like! I’ll call them topics or factors from now on.

K

movies (+/- 17,770)

users (+/- 240,000)

K

The Algorithm

B

A

- In an equation, the squeezing boils down to:
- We want to find A,B such that we can reproduce the observed ratings as
- best as we can, given that we are only given K topics.
- This reconstruction of the ratings will not be perfect (otherwise we over-fit)
- To do so, we minimize the squared error (Frobenius norm):

Prediction

- We train A,B to reproduce the observed ratings only and we ignore all the
- non-rated movie-user pairs.
- But here come the magic: after learning A,B, the product AxB will have filled
- in values for the non-rated movie-user pairs!
- It has implicitly used the information from similar users and similar movies
- to generate ratings for movies that weren’t there before.
- This is what “learning” is: we can predict things from we haven’t seen before
- by looking at old data.

The Algorithm

- We want to minimize the Error. Let’s take the gradients again.

- We could now follow the gradients again:

- But wait,
- how do we ignore the non-observed entries ?
- for netflix, this will actually be very slow, how can we make this practical ?

Stochastic Gradient Descent

- First we pick a single observed movie-user pair at random: Rmu.
- The we ignore the sums over u,m in the exact gradients, and do an update:

- The trick is that although we don’t follow the exact gradient, on average
- we do move in the correct direction.

Stochastic Gradient Descent

stochastic updates

full updates (averaged over all data-items)

- Stochastic gradient descent does not converge to the minimum, but “dances”
- around it.
- To get to the minimum, one needs to decrease the step-size as one get closer
- to the minimum.
- Alternatively, one can obtain a few samples and average predictions over them
- (similar to bagging).

Weight-Decay

- Often it is good to make sure the values in A,B do not grow too big.
- We can make that happen by adding weight decay terms which keep them small.
- Small weights are often better for generalization, but this depends on how
- many topics, K, you choose.
- The simplest weight decay results in:
- But a more aggressive variant is to replace

Incremental Updates

- One idea is to train one factor at a time.
- It’s fast
- One can monitor performance on held out test data

- A new factor is trained on the residuals of the model up till now.
- Hence, we get:
- If you fit slow (i.e. don’t go to convergence, weight decay, shrinkage) results are better.

Pre-Processing

- We are almost ready for implementation.
- But we can make a head-start if we first remove some “obvious” structure
- from the data, so the algorithm doesn’t have to search for it.
- In particular, you can subtract the user and movie means where you
- ignore missing entries.
- Nmu is the total number of observed movie-user pairs.
- Note that you can now predict missing entries by simply using:

Homework

- Subtract the movie and user means from the netflix data.
- Check theoretically and experimentally that the both user and movie means are 0.
- make a prediction for the quiz data and submit it to netflix.
- (your RMSE should be around 0.99)
- Implement the stochastic matrix factorization and submit to netflix.
- (you should get around 0.93)

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