Pareto-Efficient Hybridization forMulti-Objective Recommender

Systems

Marco Tulio Ribeiro 1,2 Anisio Lacerda 1,2

Adriano Veloso 1 Nivio Ziviani 1,2

1Universidade Federal de Minas Gerais 2Zunnit TechnologiesComputer Science Department Belo Horizonte, Brazil

Belo Horizonte, Brazil

ACM Recommender Systems 2012, Dublin, IrelandSeptember 10th, 2012

1

Pareto Efficient Hybridization forMulti-Objective Recommender Systems

I Multi-Objective:

I AccuracyI NoveltyI Diversity

I Hybridization:I Different algorithms have different strengths

I Pareto Efficient:I In a moment

2

Pareto Efficient Hybridization forMulti-Objective Recommender Systems

I Multi-Objective:

I AccuracyI NoveltyI Diversity

I Hybridization:I Different algorithms have different strengths

I Pareto Efficient:I In a moment

2

Pareto Efficient Hybridization forMulti-Objective Recommender Systems

I Multi-Objective:I AccuracyI NoveltyI Diversity

I Hybridization:I Different algorithms have different strengths

I Pareto Efficient:I In a moment

2

Pareto Efficient Hybridization forMulti-Objective Recommender Systems

I Multi-Objective:I AccuracyI NoveltyI Diversity

I Hybridization:I Different algorithms have different strengths

I Pareto Efficient:I In a moment

2

Pareto Efficient Hybridization forMulti-Objective Recommender Systems

I Multi-Objective:I AccuracyI NoveltyI Diversity

I Hybridization:I Different algorithms have different strengths

I Pareto Efficient:I In a moment

2

What’s a Good Recommendation?

I “Good” is a multifaceted concept

I Are novel recommendations goodrecommendations?

I Are accurate recommendations goodrecommendations?

I Are diverse recommendations goodrecommendations?

3

What’s a Good Recommendation?

I “Good” is a multifaceted conceptI Are novel recommendations good

recommendations?

I Are accurate recommendations goodrecommendations?

I Are diverse recommendations goodrecommendations?

3

Is Novelty Good?

I “Good” is a multifaceted conceptI Are novel recommendations good

recommendations?I Are accurate recommendations good

recommendations?I Are diverse recommendations good

recommendations?

3

Is Novelty Good?

I “Good” is a multifaceted conceptI Are novel recommendations good

recommendations?I Are accurate recommendations good

recommendations?I Are diverse recommendations good

recommendations?

3

What’s a Good Recommendation?

I “Good” is a multifaceted conceptI Are novel recommendations good

recommendations?I Are accurate recommendations good

recommendations?

I Are diverse recommendations goodrecommendations?

3

Is Accuracy Good?

I “Good” is a multifaceted conceptI Are novel recommendations good

recommendations?I Are accurate recommendations good

recommendations?I Are diverse recommendations good

recommendations?

3

Is Accuracy Good?

I “Good” is a multifaceted conceptI Are novel recommendations good

recommendations?I Are accurate recommendations good

recommendations?I Are diverse recommendations good

recommendations?

3

What’s a Good Recommendation?

I “Good” is a multifaceted conceptI Are novel recommendations good

recommendations?I Are accurate recommendations good

recommendations?I Are diverse recommendations good

recommendations?

3

Is Diversity Good?

I “Good” is a multifaceted conceptI Are novel recommendations good

recommendations?I Are accurate recommendations good

recommendations?I Are diverse recommendations good

recommendations?

3

Is Diversity Good?

I “Good” is a multifaceted conceptI Are novel recommendations good

recommendations?I Are accurate recommendations good

recommendations?I Are diverse recommendations good

recommendations?

3

Our WorkI The challenge:

I Combining multiple algorithms

I Contributions:I Domain and algorithm-independent hybridI Multi-objective in terms of accuracy, novelty

and diversity.I Adjustable compromise

4

Our WorkI The challenge:

I Combining multiple algorithmsI Contributions:

I Domain and algorithm-independent hybrid

I Multi-objective in terms of accuracy, noveltyand diversity.

I Adjustable compromise

4

Our WorkI The challenge:

I Combining multiple algorithmsI Contributions:

I Domain and algorithm-independent hybridI Multi-objective in terms of accuracy, novelty

and diversity.

I Adjustable compromise

4

Our WorkI The challenge:

I Combining multiple algorithmsI Contributions:

I Domain and algorithm-independent hybridI Multi-objective in terms of accuracy, novelty

and diversity.I Adjustable compromise

4

Weighted Aggregation

I Combine the algorithms using standardweighted aggregation

I Problem: finding the vector of weights W

I Example:W = [SVD: 2.3,TopPop: −5, ItemKNN : 1]

I Easy to add or remove algorithms

5

Weighted Aggregation

I Combine the algorithms using standardweighted aggregation

I Problem: finding the vector of weights W

I Example:W = [SVD: 2.3,TopPop: −5, ItemKNN : 1]

I Easy to add or remove algorithms

5

Weighted Aggregation

I Combine the algorithms using standardweighted aggregation

I Problem: finding the vector of weights W

I Example:W = [SVD: 2.3,TopPop: −5, ItemKNN : 1]

I Easy to add or remove algorithms

5

Weighted Aggregation

I Combine the algorithms using standardweighted aggregation

I Problem: finding the vector of weights W

I Example:W = [SVD: 2.3,TopPop: −5, ItemKNN : 1]

I Easy to add or remove algorithms

5

Evolutionary Algorithms

I A population is created with a group ofrandom individuals

I For each generation:I The individuals of the population are

evaluated (cross validation)I The best individuals are combined, mutated

or kept

I Good for search spaces where little isknown

I Domain and algorithm-independent

6

Evolutionary Algorithms

I A population is created with a group ofrandom individuals

I For each generation:I The individuals of the population are

evaluated (cross validation)I The best individuals are combined, mutated

or kept

I Good for search spaces where little isknown

I Domain and algorithm-independent

6

Evolutionary Algorithms

I A population is created with a group ofrandom individuals

I For each generation:I The individuals of the population are

evaluated (cross validation)I The best individuals are combined, mutated

or kept

I Good for search spaces where little isknown

I Domain and algorithm-independent

6

Evolutionary Algorithms

I A population is created with a group ofrandom individuals

I For each generation:I The individuals of the population are

evaluated (cross validation)I The best individuals are combined, mutated

or kept

I Good for search spaces where little isknown

I Domain and algorithm-independent

6

SPEA2

I Strength Pareto Evolutionary Algorithm[Zitzler, Laumanns and Thiele]

I Multi-Objective Evolutionary AlgorithmI Uses the Pareto Dominance conceptI Returns a Pareto FrontierI O(M 2logM), but performed offline

7

SPEA2

I Strength Pareto Evolutionary Algorithm[Zitzler, Laumanns and Thiele]

I Multi-Objective Evolutionary Algorithm

I Uses the Pareto Dominance conceptI Returns a Pareto FrontierI O(M 2logM), but performed offline

7

SPEA2

I Strength Pareto Evolutionary Algorithm[Zitzler, Laumanns and Thiele]

I Multi-Objective Evolutionary AlgorithmI Uses the Pareto Dominance conceptI Returns a Pareto FrontierI O(M 2logM), but performed offline

7

SPEA2

I Strength Pareto Evolutionary Algorithm[Zitzler, Laumanns and Thiele]

I Multi-Objective Evolutionary AlgorithmI Uses the Pareto Dominance concept

I Returns a Pareto FrontierI O(M 2logM), but performed offline

7

Pareto Dominance

I Strength Pareto Evolutionary Algorithm[Zitzler, Laumanns and Thiele]

I Multi-Objective Evolutionary AlgorithmI Uses the Pareto Dominance conceptI Returns a Pareto FrontierI O(M 2logM), but performed offline

7

Pareto Dominance

I Strength Pareto Evolutionary Algorithm[Zitzler, Laumanns and Thiele]

I Multi-Objective Evolutionary AlgorithmI Uses the Pareto Dominance conceptI Returns a Pareto FrontierI O(M 2logM), but performed offline

7

Pareto Dominance

I Strength Pareto Evolutionary Algorithm[Zitzler, Laumanns and Thiele]

I Multi-Objective Evolutionary AlgorithmI Uses the Pareto Dominance conceptI Returns a Pareto FrontierI O(M 2logM), but performed offline

7

Pareto Dominance

I Strength Pareto Evolutionary Algorithm[Zitzler, Laumanns and Thiele]

I Multi-Objective Evolutionary AlgorithmI Uses the Pareto Dominance conceptI Returns a Pareto FrontierI O(M 2logM), but performed offline

7

SPEA2

I Strength Pareto Evolutionary Algorithm[Zitzler, Laumanns and Thiele]

I Multi-Objective Evolutionary AlgorithmI Uses the Pareto Dominance conceptI Returns a Pareto Frontier

I O(M 2logM), but performed offline

7

Pareto Frontier

I Strength Pareto Evolutionary Algorithm[Zitzler, Laumanns and Thiele]

I Multi-Objective Evolutionary AlgorithmI Uses the Pareto Dominance conceptI Returns a Pareto FrontierI O(M 2logM), but performed offline

7

SPEA2

I Strength Pareto Evolutionary Algorithm[Zitzler, Laumanns and Thiele]

I Multi-Objective Evolutionary AlgorithmI Uses the Pareto Dominance conceptI Returns a Pareto FrontierI O(M 2logM), but performed offline

7

Adjusting the System Priority

I The recommender system may desire toadjust the compromise

I We do not return a single solution, but thePareto Frontier

I Given the priority of each objective, wechoose one individual from the frontier

8

Adjusting the System Priority

I The recommender system may desire toadjust the compromise

I We do not return a single solution, but thePareto Frontier

I Given the priority of each objective, wechoose one individual from the frontier

8

Adjusting the System Priority

I The recommender system may desire toadjust the compromise

I We do not return a single solution, but thePareto Frontier

I Given the priority of each objective, wechoose one individual from the frontier

8

Adjusting the System Priority

I The recommender system may desire toadjust the compromise

I We do not return a single solution, but thePareto Frontier

I Given the priority of each objective, wechoose one individual from the frontier

8

Evaluation Methodology

I Task: Top-N Item Recommendation

I Evaluation methodology similar to[Cremonesi, Koren and Turrin, RecSys 2010]

I With novelty and diversity from[Vargas and Castells, RecSys 2011]

9

Evaluation Methodology

I Task: Top-N Item RecommendationI Evaluation methodology similar to

[Cremonesi, Koren and Turrin, RecSys 2010]

I With novelty and diversity from[Vargas and Castells, RecSys 2011]

9

Evaluation Methodology

I Task: Top-N Item RecommendationI Evaluation methodology similar to

[Cremonesi, Koren and Turrin, RecSys 2010]

I With novelty and diversity from[Vargas and Castells, RecSys 2011]

9

Datasets

Movielens Last.fm

Recommends movies music

Users 6,040 992

Content 3,883 movies 176,948 artists

Ratings/Feedback 1,000,209 19,150,868

Feedback explicit implicit

Table: Summary of Datasets

10

Recommendation Algorithms

I PureSVD (50 and 150 factors)[Cremonesi, Koren and Turrin, RecSys 2010]

I KNNs: Item and User-basedI Most PopularI WRMF

[Hu et al, ICDM 2008, Pan et al ICDM 2008]

I Content-based:I Item Attribute KNN (movielens only)I User Attribute KNN

11

Recommendation Algorithms

I PureSVD (50 and 150 factors)[Cremonesi, Koren and Turrin, RecSys 2010]

I KNNs: Item and User-based

I Most PopularI WRMF

[Hu et al, ICDM 2008, Pan et al ICDM 2008]

I Content-based:I Item Attribute KNN (movielens only)I User Attribute KNN

11

Recommendation Algorithms

I PureSVD (50 and 150 factors)[Cremonesi, Koren and Turrin, RecSys 2010]

I KNNs: Item and User-basedI Most Popular

I WRMF[Hu et al, ICDM 2008, Pan et al ICDM 2008]

I Content-based:I Item Attribute KNN (movielens only)I User Attribute KNN

11

Recommendation Algorithms

I PureSVD (50 and 150 factors)[Cremonesi, Koren and Turrin, RecSys 2010]

I KNNs: Item and User-basedI Most PopularI WRMF

[Hu et al, ICDM 2008, Pan et al ICDM 2008]

I Content-based:I Item Attribute KNN (movielens only)I User Attribute KNN

11

Recommendation Algorithms

I PureSVD (50 and 150 factors)[Cremonesi, Koren and Turrin, RecSys 2010]

I KNNs: Item and User-basedI Most PopularI WRMF

[Hu et al, ICDM 2008, Pan et al ICDM 2008]

I Content-based:I Item Attribute KNN (movielens only)I User Attribute KNN

11

Hybrid Baselines

I Borda Count

I STREAM (stacking-based approach)[Bao, Bergman and Thompson, RecSys 2009]

I Weighted aggregation with equal weights

12

Hybrid Baselines

I Borda CountI STREAM (stacking-based approach)

[Bao, Bergman and Thompson, RecSys 2009]

I Weighted aggregation with equal weights

12

Hybrid Baselines

I Borda CountI STREAM (stacking-based approach)

[Bao, Bergman and Thompson, RecSys 2009]

I Weighted aggregation with equal weights

12

Some of Our Solutions - Movielens

I PO-acc:

I PO-acc2:

I PO-nov:

I PO-div:

13

14

15

16

Some of Our Solutions - Last.fm

I PO-acc:

I PO-nov:

I PO-div:

17

18

19

20

Conclusions

I A multi-objective hybridization technique forcombining recommendation algorithms

I “Tune” the system to different priority needsI Highly reproducible experiments:

I Public datasetsI Open-source implementations

(MyMediaLite, DEAP)

I Competitive with the best algorithmsaccording to each objective

21

Conclusions

I A multi-objective hybridization technique forcombining recommendation algorithms

I “Tune” the system to different priority needs

I Highly reproducible experiments:I Public datasetsI Open-source implementations

(MyMediaLite, DEAP)

I Competitive with the best algorithmsaccording to each objective

21

Conclusions

I A multi-objective hybridization technique forcombining recommendation algorithms

I “Tune” the system to different priority needsI Highly reproducible experiments:

I Public datasetsI Open-source implementations

(MyMediaLite, DEAP)

I Competitive with the best algorithmsaccording to each objective

21

Conclusions

I A multi-objective hybridization technique forcombining recommendation algorithms

I “Tune” the system to different priority needsI Highly reproducible experiments:

I Public datasetsI Open-source implementations

(MyMediaLite, DEAP)

I Competitive with the best algorithmsaccording to each objective

21

Future Work

I Test these assumptions using onlineAB-testing, in real world E-commercewebsites

I Try maximizing other objectives:I profit, stock diversity, etc

I Figuring out how often the weights need tobe re-adjusted

22

Future Work

I Test these assumptions using onlineAB-testing, in real world E-commercewebsites

I Try maximizing other objectives:I profit, stock diversity, etc

I Figuring out how often the weights need tobe re-adjusted

22

Future Work

I Test these assumptions using onlineAB-testing, in real world E-commercewebsites

I Try maximizing other objectives:I profit, stock diversity, etc

I Figuring out how often the weights need tobe re-adjusted

22

Pareto-Efficient Hybridization forMulti-Objective Recommender

Systems

Marco Tulio Ribeiro 1,2 Anisio Lacerda 1,2

Adriano Veloso 1 Nivio Ziviani 1,2

1Universidade Federal de Minas Gerais 2Zunnit TechnologiesComputer Science Department Belo Horizonte, Brazil

Belo Horizonte, Brazil

ACM Recommender Systems 2012, Dublin, IrelandSeptember 10th, 2012

23