a user-centric evaluation of recommender algorithms for an event recommendation system

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Describing the setup and results of a user-centric online experiment where 5 different recommendation algorithms are tested on a Belgium events website.

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A User-centric Evaluation of Recommender Algorithms for an Event Recommendation SystemSimon Dooms, Toon De Pessemier, Luc Martens

@sidooms

Introduction

10/23/2011 Simon Dooms - Ghent University - UCERSTI 2 2

• Online context• Cultural events• 30,000 items

Recommender System?

Concl.ResultsQuestionsAlgorithmsFeedbackExperimentIntroIntro

The Experiment

Invitation mailDAY 1

Closed questionnaireDAY 56

DAY 41

End tracking

DAY 45

Send out recs

DAY 50Reminder mail

Reminder mailDAY 28

Concl.ResultsQuestionsAlgorithmsFeedbackIntroExperimentExperiment

10/23/2011 Simon Dooms - Ghent University - UCERSTI 2 3

FeedbackConcl.ResultsQuestionsAlgorithms

FeedbackFeedbackExperimentIntro

10/23/2011 Simon Dooms - Ghent University - UCERSTI 2 4

Algorithms

Content-Based CBUser-Based Collaborative Filtering UBCFHybrid recommender CB+UBCFSingular Value Decomposition SVDRandom recommender RAND

Concl.ResultsQuestionsAlgorithmsAlgorithmsFeedbackExperimentIntro

10/23/2011 Simon Dooms - Ghent University - UCERSTI 2 5

QuestionnaireConcl.Results

QuestionsQuestionsAlgorithmsFeedbackExperimentIntro

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AccuracyFamiliarity

NoveltyDiversity

TransparencySatisfaction

TrustUsefulness

Results

612 users

Concl.ResultsResultsQuestionsAlgorithmsFeedbackExperimentIntro

10/23/2011 Simon Dooms - Ghent University - UCERSTI 2 7

232 users193 users

Concl.ResultsResultsQuestionsAlgorithmsFeedbackExperimentIntro

Average scores per question and algorithm

Accuracy Familiarity Novelty Diversity Trans. Satisfaction Trust Usefulness10/23/2011 Simon Dooms - Ghent University - UCERSTI 2 8

Concl.ResultsResultsQuestionsAlgorithmsFeedbackExperimentIntro

Average scores per question and algorithm

Accuracy Satisfaction910/23/2011 Simon Dooms - Ghent University - UCERSTI 2

CorrelationsConcl.

ResultsResultsQuestionsAlgorithmsFeedbackExperimentIntro

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AccuracyFamiliarity

NoveltyDiversity

TransparencySatisfaction

TrustUsefulness

RegressionsConcl.

ResultsResultsQuestionsAlgorithmsFeedbackExperimentIntro

10/23/2011 Simon Dooms - Ghent University - UCERSTI 2 11

Conclusions

• Online user-centric evaluation experiment• 5 popular algorithms: CB, UBCF, CB+UBCF, SVD, RAND

• CB+UBCF winner algorithm• SVD ‘loser’ algorithm• Accuracy and Transparency Satisfaction• Diversity NOT correlated with Satisfaction

Concl.Concl.ResultsQuestionsAlgorithmsFeedbackExperimentIntro

10/23/2011 Simon Dooms - Ghent University - UCERSTI 2 12

Simon Dooms, Toon De Pessemier, Luc Martens

A User-centric Evaluationof Recommender Algorithms for an Event Recommendation System

With the support of IWT Vlaanderen and FWO Vlaanderen

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