# 1 scalable collaborative filtering with jointly derived neighborhood interpolation weights robert m....

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Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights

Robert M. Bell and

Yehuda KorenSpeaker: Ming Wai <- Supervisor: Nikos Mamoulis

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Outline Introduction to CF Netflix Prize BellKor

Remove Global Effect Correlation Experiment

BellKor more

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Motivation

Personalized Recommandation

Non-personalize example: Reviewer Movie review

Book review

Food review

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Collaborative Filtering Opinions from Similar friends

How to capture the preferences of people? How to measure Similarity? How to make a prediction?

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User-Item Matrix

v1 gave j3 rating 3

j1 j2 j3 j4 j5 i

u 1 2 3 4 5 ?

v1 1 2 3 4 5 5

v2 5 4 3 2 1 1User profile

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User-based

User-based: u trust v1 more than v2

Predict ? = 5

j1 j2 j3 j4 j5 i

u 1 2 3 4 5 ?

v1 1 2 3 4 5 5

v2 5 4 3 2 1 1

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Item-based

Item-based: i similar to j5 more than other itemsPredict ? = 5

j1 j2 j3 j4 j5 i

u 1 2 3 4 5 ?

v1 1 2 3 4 5 5

v2 5 4 3 2 1 1

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Matrix nature The matrix is incomplete

1-5% of the entries have values Most of the users rated small subset of items How many movies in IMDB did you watched? Many Rating on few Items

More users than items Number of audience >> Number of Music

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More Realistic Matrix

j1 j2 j3 j4 j5 j6

v1 1 - - - - -

v2 - 2 - - 3 -

v3 - - 3 - 4 -

v4 3 - - - 5 -

v5 5 - - - 4 -

… … … … … … …Many users below

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Similarity The most popular one in CF:

Pearson Correlation Coefficient

Set of Items rated by both u and v, or the intersection of the profiles of u and v

We can find sij as well

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The following applies to any Similarity metric based on the intersection of profiles The set of items could be different for every pair

of users (the intersection) No triangle inequality (in extreme, u is similar to

v1, v2; but the intersection of v1 and v2 is empty)

Similarity nature

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Output - Prediction

Set of top-K neighbors (users who similar to u)

Again, we can do this in item-based

Very often, the similarity itself is also the weight of trust to the user v

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Systematic tendencies Accuracy of a prediction depends on

neighbors Systematic tendencies may be misleading,

and hence decreases the quality of neighbor E.g. Tendency of rating higher or lower than

normal Solution: Data Normalization

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Data Normalization

All ratings of u and v have difference 1, BUT Their patterns are actually similar Pearson Correlation Coefficient works well in this

(By subtracting the mean) For other Tendencies, or other Similarity, do

normalization may improve prediction accuracy

j1 j2 j3 j4 j5

u 1 3 3 3 4

v 2 4 4 4 5

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Summary Data Normalization (Optional) Find Neighbors (according to some similarity

metric) Assign Weights to Neighbors’ rating (often

related to the similarity) Prediction

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Netflix Prize

Contest begins October 2, 2006 and continues through at least October 2, 2011. Contest is open to anyone, anywhere (except certain countries listed below).

You have to register to enter.

Once you register and agree to these Rules, you’ll have access to the Contest training data and qualifying test sets.

To qualify for the $1,000,000 Grand Prize, the accuracy of your submitted predictions on the

qualifying set must be at least 10% better than the accuracy Cinematch can achieve on the same training data set at the

start of the Contest.

To qualify for a year’s $50,000 Progress Prize the accuracy of any of your submitted predictions that

year must be less than or equal to the accuracy value established by the judges the preceding year.

To win and take home either prize, your qualifying submissions must have the largest accuracy improvement verified by the

Contest judges, you must share your method with (and non-exclusively license it to) Netflix, and you must describe to the world how you did it and

why it works.

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Netflix Prize Data 480189 users 17770 movies 100 million ratings Probe set

some selected entries which we know the real rating we can test to see the accuracy of our algorithm

Quiz set Some selected entries which we DON’T know the real

rating Predict these entries well wins $1,000,000

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Robert M. Bell and Yehuda Koren

Scalable Collaborative Filtering with Jointly

Derived Neighborhood Interpolation Weights

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Bell and Koren's concerns

Similarity function

Example:

an item is predicted perfectly by a subset of items

(the authors think in item-based way)

The subset should take all weight

Impossible in Pearson

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Bell and Koren's concerns Similarity function

Example:

i j2 j3 j4 j5

v1 4 4 1 4 5

v2 1 1 1 1 4

v3 4 4 1 1 5

j2 predict ia perfectly, we may give all weight to j2

Similarity based weighting have to give some weight to the not-so-similar items

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Bell and Koren's concerns

Ignorence of interactions among neighbours

Example:

Lord of the Rings 1-3

Triple counting Lord of the Rings

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Bell and Koren's concerns

Weights must sum to one If no good neighbor, the weights should be ignored

and predict by the mean

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Global Effects

The tendencies

Example:

Harsh user tends to rated lower, and vise versa

The harshness may change over time

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Global Effects u will be considered as more similar to v2 than

v3 after centering by subtracting the user mean

i1 i2 i3 i4 i5

u 3 3 4 5 3

v2 2 2 3 4 2

v3 3 3 3 3 3

i1 i2 i3 i4 i5

u -0.6 -0.6 0.4 1.4 -0.6

v2 -0.6 -0.6 0.4 1.4 -0.6

v3 0.0 0.0 0.0 0.0 0.0

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0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 100 200 300 400 500 600

Support

Rating

Global Effects – Hypothetical Example

Number of users rated this item

This user rated an item of 474 supports the rating 2

Each dot is a entry in the matrix of some user

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Global Effects – Hypothetical Example

Number of users rated this item

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 100 200 300 400 500 600

Support

Rating

Residual = rating

after removing the

effect of support

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Global Effects

When considering the user effect, x's are 1 and

the result is essentially centering the data

among the user mean

Same in item effect

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Global Effects

Time(user)1/2

Time(user) = (date of the rating - date of 1st rating)

Age of users

User's rating style may change over time

Time(movie)1/2

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Global Effects

User x Movie Average

User x Movie Support (number of users rated

the movie)

How a user is affected by the popularity of the

movies

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Neighbors

Item Neighors Item-based

K of the Items -> N(i; u)

K Items of highest Pearson Correlation

Coefficient

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Find the weights

Wikipedia

Least-squares estimation of

linear regression coefficients

http://en.wikipedia.org/wiki/Least-

squares_estimation_of_linear_re

gression_coefficients

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References [1] G. Adomavicius and A. Tuzhilin, “Towards the Next Generation of Recommender Systems: A Survey of the State-of-

the-Art and Possible Extensions”, IEEE Transactions on Knowledge and Data Engineering 17 (2005), 634–749.

[2] R. M. Bell, Y. Koren and C. Volinsky, “Modeling Relationships at Multiple Scales to Improve Accu- racy of Large

Recommender Systems”, Proc. 13th ACM SIGKDD International Conference on Knowl- edge Discovery and Data Mining,

2007.

[3] J. Bennet and S. Lanning, “The Netflix Prize”, KDD Cup and Workshop, 2007.

[4] B. Efron and C. Morris, “Data analysis using Stein’s estimator and its generalization”, Journal American Statistical

Association 70 (1975), 311–319.

[5] S. Funk, “Netflix Update: Try This At Home”, sifter.org/ Ssimon/journal/20061211. html, 2006.

[6] D. Goldberg, D. Nichols, B. M. Oki and D. Terry, “Us- ing Collaborative Filtering to Weave an Information Tapestry”,

Communications of the ACM 35 (1992), 61–70.

[7] K. Goldberg, T. Roeder, D. Gupta and C. Perkins, “Eigentaste: A Constant Time Collaborative Filtering Algorithm”,

Information Retrieval 4 (2001), 133–151.

[8] J. L. Herlocker, J. A. Konstan, A. Borchers and John Riedl, “An Algorithmic Framework for Performing Collaborative

Filtering”, Proc. 22nd ACM SIGIR Con- ference on Information Retrieval, pp. 230–237, 1999.

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References [9] J. Konstan, B. Miller, D. Maltz, J. Herlocker, L. Gor- don and J. Riedl, “GroupLens: Applying Collabora- tive Filtering to

Usenet News”, Communications of the ACM 40 (1997), 77–87, www.grouplens.org.

[10] C.L. Lawson and B. J. Hanson, Solving Least Squares Problems, Prentice-Hall, 1974. n

[11] G. Linden, B. Smith and J. York, “Amazon.com Rec- ommendations: Item-to-item Collaborative Filtering”, IEEE Internet

Computing 7 (2003), 76–80.

[12] J. Nocedal and S. Wright, Numerical Optimization, Springer, 1999.

[13] R. Salakhutdinov, A. Mnih, and G. Hinton, “Re- stricted Boltzmann Machines for Collaborative Filter- ing”, Proc. 24th

Annual International Conference on Machine Learning, 2007.

[14] B. M. Sarwar, G. Karypis, J. A. Konstan, and J. Riedl, “Application of Dimensionality Reduction in Recom- mender

System – A Case Study”, WEBKDD’2000.

[15] B. Sarwar, G. Karypis, J. Konstan and J. Riedl, “Item- based Collaborative Filtering Recommendation Algo- rithms”,

Proc. 10th International Conference on the World Wide Web, pp. 285-295, 2001.

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More Tricks by the authors Robert M. Bell, Yehuda Koren and Chris

Volinsky

Modeling Relationships at Multiple Scales to Improve Accuracy of Large Recommender Systems

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Experimental Result

Accuracy decreases when more neighbors

Reflect interdependencies among neighbors, so RMSE keep decreasing

When consider User similarity

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Factorization m x n matrix R m x f matrix P n x f matrix Q Rf = PQT

Singular Value Decomposition (SVD) minimize || R-Rf ||F (Frobenius norm)

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Factorization Main idea

Use SVD in opposite way Let SVD factorize the user-item matrix by f

factors rui is predicted as Rf

ui (Rf = PQT)

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Factorization

Minimizing ∑i(resui – PufQif)2

By setting proper Puf

Find Factor by Factor (columns of P and Q)

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Factorization

resui is shrank by multiplying a factor

nui is the support of this rui = min(#user rated i, #item rated by u). Shrink more if support less

αis a constant

f is the factor number. Earlier found factor (small f) explain higher variations; later factor (large f) lower variations of the data

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Winning the Prize

Combine the results according to the faith in the method.

The faith is derived according to how well the error is minimized

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References [1] G. Adomavicius and A. Tuzhilin, “Towards the Next Generation of Recommender

Systems: A Survey of the State-of-the-Art and Possible Extensions”, IEEE Transactions on Knowledge and Data Engineering 17 (2005), 634–749.

[2] R. Bell and Y. Koren, “Improved Neighborhood-based Collaborative Filtering”, submitted, 2007.

[3] S. Deerwester, S. Dumais, G. W. Furnas, T. K. Landauer and R. Harshman, “Indexing by Latent Semantic Analysis”, Journal of the Society for Information Science 41 (1990), 391–407.

[4] D. Goldberg, D. Nichols, B. M. Oki and D. Terry, “Using Collaborative Filtering to Weave an Information Tapestry”, Communications of the ACM 35 (1992), 61–70.

[5] K. Goldberg, T. Roeder, D. Gupta and C. Perkins, Eigentaste: A Constant Time Collaborative Filtering Algorithm”, Information Retrieval 4 (2001), 133–151.

[6] G.H. Golub and C.F.Van Loan, Matrix Computations, Johns Hopkins University Press, 1996.

[7] J. L. Herlocker, J. A. Konstan, A. Borchers and John Riedl, “An Algorithmic Framework for Performing Collaborative Filtering”, Proc. 22nd ACM SIGIR Conference on Information Retrieval, pp. 230–237, 1999.

[8] D. Kim and B. Yum, “Collaborative Filtering Based on Iterative Principal Component Analysis”, Expert Systemswith Applications 28 (2005), 823–830.

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References [9] J. Konstan, B. Miller, D. Maltz, J. Herlocker, L. Gordon and J. Riedl, “GroupLens:

Applying Collaborative Filtering to Usenet News”, Communications of the ACM 40 (1997), 77–87, www.grouplens.org.

[10] Netflix prize -www.netflixprize.com. [11] G. Linden, B. Smith and J. York, “Amazon.com Recommendations: Item-to-item

Collaborative Filtering”, IEEE Internet Computing 7 (2003), 76–80. [12] J. Nocedal and S. Wright, Numerical Optimization, Springer (1999). [13] S. Roweis, “EM Algorithms for PCA and SPCA”, Advances in Neural Information

Processing Systems 10, pp. 626–632, 1997. [14] B. M. Sarwar, G. Karypis, J. A. Konstan, and J. Riedl, “Application of Dimensionality

Reduction in Recommender System – A Case Study”, WEBKDD’2000. [15] B. Sarwar, G. Karypis, J. Konstan and J. Riedl, “Item-based Collaborative Filtering

Recommendation Algorithms”, Proc. 10th International Conference on the World Wide Web, pp. 285-295, 2001.

[16] R. Tibshirani, “Regression Shrinkage and Selection via the Lasso”, Journal of the Royal Statistical Society B 58 (1996).

[17] J. Wang, A. P. de Vries and M. J. T. Reinders,“Unifying User-based and Item-based Collaborative Filtering Approaches by Similarity Fusion”, Proc. 29th ACM SIGIR Conference on Information Retrieval, pp. 501–508, 2006.