Pareto-Efficient Hybridization for Multi-Objective Recommender Systems

transcript

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

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

I Multi-Objective:

I AccuracyI NoveltyI Diversity

I Multi-Objective:I AccuracyI NoveltyI Diversity

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?

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

recommendations?

I Are accurate recommendations goodrecommendations?

Is Novelty Good?

recommendations?I Are accurate recommendations good

recommendations?I Are diverse recommendations good

recommendations?

Is Novelty Good?

recommendations?

Is Accuracy Good?

recommendations?

Is Accuracy Good?

recommendations?

Is Diversity Good?

recommendations?

Is Diversity Good?

recommendations?

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

I Combining multiple algorithmsI Contributions:

I Domain and algorithm-independent hybrid

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

I Adjustable compromise

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

and diversity.

I Adjustable compromise

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

and diversity.I Adjustable compromise

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

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

or kept

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

I Multi-Objective Evolutionary Algorithm

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

I Multi-Objective Evolutionary AlgorithmI Uses the Pareto Dominance concept

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

Pareto Dominance

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

I O(M 2logM), but performed offline

Pareto Frontier

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

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]

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

[Cremonesi, Koren and Turrin, RecSys 2010]

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

[Cremonesi, Koren and Turrin, RecSys 2010]

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

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

I KNNs: Item and User-based

I Most PopularI WRMF

I KNNs: Item and User-basedI Most Popular

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

Hybrid Baselines

I Borda Count

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

I Weighted aggregation with equal weights

Hybrid Baselines

I Borda CountI STREAM (stacking-based approach)

[Bao, Bergman and Thompson, RecSys 2009]

Hybrid Baselines

I Borda CountI STREAM (stacking-based approach)

[Bao, Bergman and Thompson, RecSys 2009]

Some of Our Solutions - Movielens

I PO-acc:

I PO-acc2:

I PO-nov:

I PO-div:

Some of Our Solutions - Last.fm

I PO-acc:

I PO-nov:

I PO-div:

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

Conclusions

I “Tune” the system to different priority needs

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

(MyMediaLite, DEAP)

Conclusions

(MyMediaLite, DEAP)

Conclusions

(MyMediaLite, DEAP)

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

Future Work

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

Pareto-Efficient Hybridization for Multi-Objective Recommender Systems

Technology