Movie #7614 movie #3250 movie #15868 Lessons from the Netflix Prize Yehuda Koren The BellKor team (with Robert Bell & Chris Volinsky)

movie #7614 movie #3250

movie #15868

Lessons from the Netflix Prize

Yehuda KorenThe BellKor team

(with Robert Bell & Chris Volinsky)

We Know What You OughtTo Be Watching This

Summer

Collaborative filtering• Recommend items based on past transactions of users

• Analyze relations between users and/or items

• Specific data characteristics are irrelevant

– Domain-free: user/item attributes are not necessary

– Can identify elusive aspects

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315?

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683?

Training data Test data

Movie rating data

Netflix Prize• Training data

– 100 million ratings– 480,000 users– 17,770 movies– 6 years of data: 2000-2005

• Test data– Last few ratings of each user (2.8 million)– Evaluation criterion: root mean squared error (RMSE) – Netflix Cinematch RMSE: 0.9514

• Competition– 2700+ teams– $1 million grand prize for 10% improvement on Cinematch result– $50,000 2007 progress prize for 8.43% improvement

Overall rating distribution

• Third of ratings are 4s• Average rating is 3.68

From TimelyDevelopment.com

#ratings per movie

• Avg #ratings/movie: 5627

#ratings per user

• Avg #ratings/user: 208

Average movie rating by movie count

• More ratings to better movies

From TimelyDevelopment.com

Most loved movies

TitleAvg ratingCount

The Shawshank Redemption 4.593 137812

Lord of the Rings: The Return of the King4.545 133597

The Green Mile 4.306 180883

Lord of the Rings: The Two Towers 4.460 150676

Finding Nemo 4.415 139050

Raiders of the Lost Ark 4.504 117456

Forrest Gump 4.299 180736

Lord of the Rings: The Fellowship of the ring4.433 147932

The Sixth Sense 4.325 149199

Indiana Jones and the Last Crusade 4.333 144027

Important RMSEs

Grand Prize: 0.8563; 10% improvement

BellKor: 0.8693; 8.63% improvement

Cinematch: 0.9514; baseline

Movie average: 1.0533

User average: 1.0651

Global average: 1.1296

Inherent noise ???? :

Personalization

erroneous

accurate

Challenges

• Size of data– Scalability

– Keeping data in memory

• Missing data– 99 percent missing

– Very imbalanced

• Avoiding overfitting• Test and training data differ significantly movie #16322

• Use an ensemble of complementing predictors• Two, half tuned models worth more than a single,

fully tuned model

The BellKor recommender system

Effect of ensemble size

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#Predictors

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• Use an ensemble of complementing predictors• Two, half tuned models worth more than a single,

fully tuned model• But:

Many seemingly different models expose similar characteristics of the data, and won’t mix well

• Concentrate efforts along three axes...

The BellKor recommender system

The first axis:• Multi-scale modeling of the data• Combine top level, regional modeling of

the data, with a refined, local view:– k-NN: Extracting local patterns– Factorization: Addressing regional

effects

The three dimensions of the BellKor system

Global effects

Factorization

k-NN

Multi-scale modeling – 1st tier

• Mean rating: 3.7 stars• The Sixth Sense is 0.5 stars above

avg• Joe rates 0.2 stars below avgBaseline estimation:

Joe will rate The Sixth Sense 4 stars

Global effects:

Multi-scale modeling – 2nd tier

• Both The Sixth Sense and Joe are placed high on the “Supernatural Thrillers” scale

Adjusted estimate:Joe will rate The Sixth Sense 4.5 stars

Factors model:

Multi-scale modeling – 3rd tier

• Joe didn’t like related movie Signs

Final estimate:Joe will rate The Sixth Sense 4.2 stars

Neighborhood model:

The second axis:• Quality of modeling• Make the best out of a model • Strive for:

– Fundamental derivation– Simplicity– Avoid overfitting– Robustness against #iterations,

parameter setting, etc. • Optimizing is good, but don’t overdo it!


global

local

quality

• The third dimension will be discussed later...

• Next:Moving the multi-scale view along the quality axis


global

local

quality

???

• Earliest and most popular collaborative filtering method• Derive unknown ratings from those of “similar” items (movie-movie

variant)• A parallel user-user flavor: rely on ratings of like-minded users (not

in this talk)

Local modeling through k-NN

k-NN

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-unknown rating -rating between 1 to 5

k-NN

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-estimate rating of movie 1 by user 5

k-NN

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Neighbor selection:Identify movies similar to 1, rated by user 5

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k-NN

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Compute similarity weights:s13=0.2, s16=0.3

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k-NN

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Predict by taking weighted average:(0.2*2+0.3*3)/(0.2+0.3)=2.6

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Properties of k-NN

• Intuitive• No substantial preprocessing is required• Easy to explain reasoning behind a recommendation• Accurate?

Grand Prize: 0.8563

BellKor: 0.8693

Cinematch: 0.9514





0.96

0.91

k-NN on the RMSE scale

k-NN

erroneous

accurate

k-NN - Common practice1. Define a similarity measure between items: sij

2. Select neighbors -- N(i;u): items most similar to i, that were rated by u

3. Estimate unknown rating, rui, as the weighted average:

( ; )

( ; )

ij uj ujj N i uui ui

ijj N i u

s r br b

s

baseline estimate for rui

k-NN - Common practice1. Define a similarity measure between items: sij

2. Select neighbors -- N(i;u): items similar to i, rated by u

3. Estimate unknown rating, rui, as the weighted average:

Problems:

1. Similarity measures are arbitrary; no fundamental justification

2. Pairwise similarities isolate each neighbor; neglect interdependencies among neighbors

3. Taking a weighted average is restricting; e.g., when neighborhood information is limited

( ; )

( ; )

ij uj ujj N i uui ui

ijj N i u

s r br b

s

(We allow )

Interpolation weights

• Use a weighted sum rather than a weighted average:

( ; )

1ijj N i u

w

( ; )ui ui ij uj ujj N i ur b w r b

• Model relationships between item i and its neighbors• Can be learnt through a least squares problem from all

other users that rated i:

2

( ; )Minw vi vi ij vj vjv u j N i u

r b w r b

Interpolation weights

2

( ; )Minw vi vi ij vj vjv u j N i u

r b w r b

• Interpolation weights derived based on their role; no use of an arbitrary similarity measure

• Explicitly account for interrelationships among the neighbors

Challenges:• Deal with missing values• Avoid overfitting• Efficient implementation

Mostly unknown

Estimate inner-products among movie ratings

Geared towards females

Geared towards

males

serious

escapist

The PrincessDiaries

The Lion King

Braveheart

Lethal Weapon

Independence Day

AmadeusThe Color Purple

Dumb and Dumber

Ocean’s 11

Sense and Sensibility

Gus

Dave

Latent factor models

Latent factor models

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2.1-.4.61.72.4.9-.3.4.8.7-.6.1

~

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item

s

users

users

A rank-3 SVD approximation

Estimate unknown ratings as inner-products of factors:

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Latent factor models13554

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~

Properties:• SVD isn’t defined when entries are unknown use

specialized methods• Very powerful model can easily overfit, sensitive to

regularization• Probably most popular model among contestants

– 12/11/2006: Simon Funk describes an SVD based method– 12/29/2006: Free implementation at timelydevelopment.com

Grand Prize: 0.8563

BellKor: 0.8693

Cinematch: 0.9514





0.93

0.89

Factorization on the RMSE scale

factorization

Our approach

• User factors: Model a user u as a vector pu ~ Nk(, )

• Movie factors:Model a movie i as a vector qi ~ Nk(γ, Λ)

• Ratings:Measure “agreement” between u and i: rui ~ N(pu

Tqi, ε2)

• Maximize model’s likelihood:– Alternate between recomputing user-factors,

movie-factors and model parameters– Special cases:

• Alternating Ridge regression• Nonnegative matrix factorization

Combining multi-scale views

global effects

regional effects

local effects

Residual fitting

factorization

k-NN

Weighted average

A unified model

k-NN

factorization

Localized factorization model

• Standard factorization: User u is a linear function parameterized by pu

rui = puTqi

• Allow user factors – pu – to depend on the item being predicted

rui = pu(i)Tqi

• Vector pu(i) models behavior of u on items like i

RMSE vs. #Factors

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#Factors

RM

SE Factorization

Localized factorization

Results on Netflix Probe set

More accurate

Top contenders for Progress Prize 2007

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How low can he go?

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Gravity

BellKor

Grand prize

multi-scale modeling

• Can exploit movie titles and release year• But movies side is pretty much covered anyway...• It’s about the users!• Turning to the third dimension...

Seek alternative perspectives of the data

global

local

quality

???

The third dimension of the BellKor system

• A powerful source of information:Characterize users by which movies they rated, rather than how they rated

A binary representation of the data

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The third dimension of the BellKor system

movie #17270

Great news to recommender systems:• Works even better on real life datasets (?)• Improve accuracy by exploiting implicit

feedback• Implicit behavior is abundant and easy to

collect:– Rental history– Search patterns– Browsing history– ...

• Implicit feedback allows predicting personalized ratings for users that never rated!

binaryimplicit

global

local

quality


Where do you want to be?• All over the global-local axis• Relatively high on the quality axis• Can we go in between the explicit-

implicit axis?• Yes! Relevant methods:

– Conditional RBMs [ML@UToronto]– NSVD [Arek Paterek]– Asymmetric factor models

ratingsexplicit

LessonsWhat it takes to win:1. Think deeper – design better algorithms2. Think broader – use an ensemble of multiple predictors3. Think different – model the data from different perspectives

At the personal level:1. Have fun with the data2. Work hard, long breath3. Good teammates

Rapid progress of science:1. Availability of large, real life data2. Challenge, competition3. Effective collaboration

movie #13043

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Yehuda Koren

AT&T Labs – Research

[email protected]

BellKor homepage:www.research.att.com/~volinsky/netflix/

Movie #7614 movie #3250 movie #15868 Lessons from the Netflix Prize Yehuda Koren The BellKor team (with Robert Bell & Chris Volinsky)

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