Netflix Prize Solution: A Matrix Factorization Approach By Atul S. Kulkarni [email protected]...
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Transcript of Netflix Prize Solution: A Matrix Factorization Approach By Atul S. Kulkarni [email protected]...
Netflix Prize Solution: A Matrix Factorization Approach
ByAtul S. Kulkarni
[email protected] student
University of Minnesota Duluth
Agenda
• Problem Description• Netflix Data• Why is it a tough nut to crack?• Overview of methods already applied to this problem• Overview of the Paper• Details of the method• How does this method works for the Netflix problem• My implementation• Results• Q and A?
Netflix Prize Problem
• Given a set of users with their previous ratings for a set of movies, can we predict the rating they will assign to a movie they have not previously rated?
• Defined at http://www.netflixprize.com//index• Seeks to improve the Cinematch’s (Netflix’s existing
movie recommender system) prediction performance by 10%.
• How is the performance measured? – Root Mean Square Error (RMSE)
• Winner gets a prize of 1 Million USD.
Problem Description
• Recommender Systems– Use the knowledge about preference of a group of
users about a certain items and help predict the interest level for other users from same community. [1]
• Collaborative filtering– Widely used method for recommender systems– Tries to find traits of shared interest among users
in a group to help predict the likes and dislikes of the other users within the group. [1]
Why is this problem interesting?
• Used by almost every recommender system today– Amazon– Yahoo– Google– Netflix– …
Netflix Data
• Netflix released data for this competition• Contains nearly 100 Million ratings • Number of users (Anonymous) = 480,189• Number of movies rated by them = 17,770• Training Data is provided per movie• To verify the model developed without submitting
the predictions to Netflix “probe.txt” is provided• To submit the predictions for competition
“qualifying.txt” is used
Netflix Data in Pictures
• These pictures are taken as is from [5]
1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.6 3.8 4 4.2 4.4 4.6 4.8 50
5000
10000
15000
20000
25000
30000
35000
40000
45000
Num. Users with Avg. Rating of
Netflix Data in Pictures Contd.
19091923
19241922
19261929
19301935
19341942
19401947
19461951
19521955
19571970
19621969
19751972
19781982
19841987
19901993
20051998
20002004
0
200
400
600
800
1000
1200
1400
1600
Number of movies per year
Netflix Data in Pictures Contd.
4
9
28
33
26
Distribution of ratings
12345
Netflix Data
• Data in the training file is per movie– It looks like this
Movie#Customer#,Rating,Date of RatingCustomer#,Rating,Date of RatingCustomer#,Rating,Date of Rating
- Example 4:1065039,3,2005-09-061544320,1,2004-06-28410199,5,2004-10-16
Netflix Data
Data points in the “probe.txt” looks like this (Have answers)
Movie#Customer#Customer#
1:3087826478711283744
Data in the qualifying.txt looks like this (No answers)
Movie#Customer#, DateofRatingCustomer#, DateofRating
1:1046323,2005-12-191080030,2005-12-231830096,2005-03-14
Hard Nut to Crack?
• Why is this problem such a difficult one?– Total ratings possible = 480,189 (user) * 17,770 (movies) = 8532958530 (8.5
Billion)– Total available = 100 Million– The User x Movies matrix has 8.4 Billion entries
missing– Consider the problem as Least Square problem– We can consider this problem by representing it as
system of equation in a matrix
Technically tough as well
• Huge memory requirements• High time requirements• Because we are using only ~100 Million of
possible 8.5 Billion ratings the predictors have some error in their weights (small training data)
Various Methods Employed for Netflix Prize Problem
• Nearest Neighbor methods– k-NN with variations
• Matrix factorization– Probabilistic Latent Semantic Analysis– Probabilistic Matrix Factorization– Expectation Maximization for Matrix Factorization– Singular Value Decomposition– Regularized Matrix Factorization
[2]
The Paper
• Title: “Improving regularized singular value decomposition for collaborative filtering” - Arkadiusz Paterek, Proceedings of KDD Cup and Workshop, 2007. [3]
• Uses Algorithm described by Simon Funk (Brandyn Webb) in [4].
• The algorithm revolves around regularized Singular Value Decomposition (SVD) described in [4] and suggests some interesting use of biases to it to improve performance.
• It also proposes some methods for post processing of the features extracted from the SVD.
• It compares the various combinations of methods suggested in the paper for the Netflix Data.
Singular Value Decomposition
• Consider the given problem as a Matrix of Users x Movies A
or • Movies x Users• Show are the two
examples• What do we do with
this representation?
M1 M2 M3 M4 M5 M6
U1 2 4 5 5 1
U2 3 5 1 5
U3 2 4 5 5
U1 U2 U3
M1 2 2
M2 4
M3 5 3 4
M4 5 5 5
M5 1
M6 1 5 5
Singular Value Decomposition
• Method of Matrix Factorization
• Applicable to rectangular matrices and square alike
• Decomposes the matrix in to 3 component matrices whose product approximates the original matrix
• E.g.• D $d
[1] 13.218989 4.887761 1.538870• U $u [,1] [,2] [,3]
[1,] -0.5606779 0.8192382 -0.1203705[2,] -0.5529369 -0.4786352 -0.6820331[3,] -0.6163612 -0.3158436 0.7213472
• V $v [,1] [,2] [,3][1,] -0.17808307 0.20598164 0.78106201[2,] -0.16965834 0.67044040 -0.31288023[3,] -0.52406769 0.28579770 0.15429276[4,] -0.65435261 0.02532797 -0.26336364[5,] -0.04182898 -0.09792523 -0.44320373[6,] -0.48469427 -0.64511243 0.04951659
Can we recover original Matrix?
• Yes. (Well almost!) Here is how.• We will Multiply the 3 Matrices U*D*VT
• We get – A* ~= A.• [,1] [,2] [,3] [,4] [,5] [,6][1,] 2.000000e+00 4.000000e+00 5 5 -1.557185e-17 1[2,] -8.564655e-16 -1.221706e-15 3 5 1.000000e+00 5[3,] 2.000000e+00 -1.231356e-15 4 5 1.757492e-16 5
• We can see this is an Approximation of the original matrix.
How do we use SVD?
• We use the 2 matrices U and V to estimate the original matrix A.
• So what happened to the diagonal matrix D?• We train our method on the given training set
and learn by rolling the diagonal matrix in the two matrices.
• We do U * VT and obtain A’.• Error = ∀i∀jAij’ – Aij.
Algorithm variations covered in this paper
• Simple Predictors• Regularized SVD• Improved Regularized SVD (with Biases)• Post processing SVD with KNN• Post processing SVD with kernel ridge regression• K-means• Linear model for each item• Decreasing the number of Parameters
The SVD Algorithm from paper [3,4,6]
• Initialize 2 arrays movieFeatures (U) and customerFeatures (V) to very small value 0.1
• For every feature# in featuresUntil minimum iterations are done or RMSE is not improving more than
minimum improvement For every data point in training set //data point has custID and movieID
prating = customerFeatures[feature#][custID] * movieFeatures [feature#][movieID] //Predict the rating
error = originalrating - prating //Find the errorsquareerrsum += error * error //Sum the squared error for RMSE.cf = customerFeatures[feature#][custID] //locally copy current feature
value mf = movieFeatures [feature#][movieID] //locally copy current feature value
Contd.
Algorithm contd. customerFeatures[feature#][custID] += learningrate *(error * mf – regularizationfactor * cf) //Rolling the ERROR in to the features
movieFeatures [feature#][movieID] += learningrate *(error * cf – regularizationfactor * mf) //Rolling the ERROR in to the feature
RMSE = (squareerrsum / total number of data points) // Calculate RMSE• Now we do the testing• For every test point with custID and movieID
For every feature# in Featurespredictedrating += customerFeatures[feature#][custID] *
movieFeatures [feature#][movieID]
• Caveats – clip the ratings in the range (1, 5) predicted rating might go out of bounds
• “Regularization factor” is introduced by Brandyn Webb in [4] to reduce the over fitting
Variation: Improved Regularized SVD
• That was regularized SVD• Improved Regularized SVD with Biases
– Predict the rating with 2 added biases Ci per customer and Dj per movie
• Rating = Ci + Dj + coustomerFeatures[featue#][i] * movieFeatures[Feature#][j]
– During training update the biases as • Ci += learningrate * (err – regularization(Ci + Dj – global_mean))
• Dj += learningrate * (err – regularization(Ci + Dj – global_mean)) • Learningrate = .001, regularization = 0.05, global_mean = 3.6033
Variation: KNN for Movies
• Post processing with KNN– On the Regularized SVD movieFeature matrix we
run cosine similarity between 2 vectors similarity = movieFeature[movieID1]T * movieFeature[movieID2]
||movieFeature[movieID1]||*||movieFeature[movieID2]||
– Using this similarity measure we build a neighborhood of 1 nearest movies and predict rating of the nearest movie as the predicted rating
Experimentation Strategy by author
• Select 1.5% - 15% of the probe.txt as hold-out set or test set.
• Train all models on rest of the ratings• All models predict the ratings• Merge the results using linear regression on
the test set• Combining two methods for initial prediction
& then performing linear regression
Results from the Paper[2]Predictor Test RMSE with
BASICTest RMSE with BASIC and RSVD2
Cumulative Test RMSE
BASIC .9826 .9039 .9826
RSVD .9024 .9018 .9094
RSVD2 .9039 .9039 .9018
KMEANS .9410 .9029 .9010
SVD_KNN .9525 .9013 .8988
SVD_KRR .9006 .8959 .8933
LM .9506 .8995 .8902
NSVD1 .9312 .8986 .8887
NSVD2 .9590 .9032 .8879
SVD_KRR * NSVD1 - - .8879
SVD_KRR * NSVD2 - - .8877
Replicated from the paper as is
My Experiments
• I am trying out the regularized SVD method and Improved Regularized SVD method with qualifying.txt, probe.txt
• Also, going to implement first 3 steps of the author’s experimentation strategy (in my case I will predict with regularized SVD and Improved regularized SVD)
• If time permits might try SVD KNN method• I am also varying some parameters like learning rate,
number of features, etc. to see its effect on the results.• I shall have all my results posted on the web site soon
Questions?
References1. Herlocker, J, Konstan, J., Terveen, L., and Riedl, J.
Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems 22 (2004), ACM Press, 5-53.
2. Gábor Takács, István Pilászy, Bottyán Németh, Domonkos Tikk Scalable Collaborative Filtering Approaches for Large Recommender Systems. JMLR Volume 10 :623--656, 2009.
3. Arkadiusz Paterek, Improving regularized singular value decomposition for collaborative filtering - Proceedings of KDD Cup and Workshop, 2007.
4. http://sifter.org/~simon/journal/20061211.html5. http://www.igvita.com/2006/10/29/dissecting-the-netflix-dataset/6. G. Gorrell and B. Webb. Generalized hebbian algorithm for incremental latent
semantic analysis. Proceedings of Interspeech, 2006.
Thanks for your time!
Atul S. [email protected]