IntroductionOur Purpose

Evaluation and ResultsConclusion

Improving a Recommender System ThroughIntegration of User Profiles: a Semantic Approach

Jonathas Magalhaes, Cleyton Souza, Priscylla Silva,Evandro Costa and Joseana Fechine

TIPS GroupFederal University of Campina Grande, Brazil

Federal University of Alagoas, Brazil

J. Magalhaes, C. Souza, P. Silva, E. Costa and J. Fechine SRS – UMAP – 2012 1

IntroductionOur Purpose

Evaluation and ResultsConclusion

Introduction

J. Magalhaes, C. Souza, P. Silva, E. Costa and J. Fechine SRS – UMAP – 2012 2

IntroductionOur Purpose

Evaluation and ResultsConclusion

Introduction

Our purpose

J. Magalhaes, C. Souza, P. Silva, E. Costa and J. Fechine SRS – UMAP – 2012 3

IntroductionOur Purpose

Evaluation and ResultsConclusion

Introduction

Questions:

Q1: Will the profiles integration improve the quality ofrecommendation of a recommender system?

Q2:Will the profiles integration reduce the cold-start problem in arecommender system?

J. Magalhaes, C. Souza, P. Silva, E. Costa and J. Fechine SRS – UMAP – 2012 4

IntroductionOur Purpose

Evaluation and ResultsConclusion

The User Profiles Integration

~pu = integration( ~ps1u , ~p

s2u , ...,

~p|S |u ) =

∑s∈S

~psu · asu, (1)

where asju :

Represents the importance of the data source sj to the integratedprofile;

Is computed by the activity of the user u in the source data sj :

asu =tnow − t|Iu,s | +

∑|Iu,s |−1j=1 (tj+1 − tj)

|Iu,s |+ 1. (2)

J. Magalhaes, C. Souza, P. Silva, E. Costa and J. Fechine SRS – UMAP – 2012 5

IntroductionOur Purpose

Evaluation and ResultsConclusion

Evaluation

The case study focused in the Software Engineering Domain,using an domain ontology;

The terms of ontology were learn using 100 papers for eachconcept;

In this case study participated ten researches, where five werenew users;

Their profiles were built using three different strategies:

Using the Lattes CV ;Using the Linkedin;The integration of both.

J. Magalhaes, C. Souza, P. Silva, E. Costa and J. Fechine SRS – UMAP – 2012 6

IntroductionOur Purpose

Evaluation and ResultsConclusion

Evaluation

We coupled the three different strategies in a RecommenderSystem of papers;

The papers (40,855) were loaded from the digital libraryCiteerSeeX;

We recommended 15 papers, five for each strategy;

The users evaluated the recommended papers in a scale from 0to 4;

We compare the strategies using the metric NormalizedDiscounted Cumulative Gain (nDCG).

J. Magalhaes, C. Souza, P. Silva, E. Costa and J. Fechine SRS – UMAP – 2012 7

IntroductionOur Purpose

Evaluation and ResultsConclusion

Results

With all users:

Figure: Comparing the three strategies to built the user profile.

J. Magalhaes, C. Souza, P. Silva, E. Costa and J. Fechine SRS – UMAP – 2012 8

IntroductionOur Purpose

Evaluation and ResultsConclusion

Evaluation

With all users:

t1 with Ha,1,1: integrated is greater than lattes

t2 with Ha,1,2: integrated is greater than linkedin

Table: Comparison between the three strategies using the Student’s t-test(α = 0.05) with all users.

T-test Alternative p-value MeaningHypothesis

t1 Ha,1,1 0.03152 Ha,1,1 accepted

t2 Ha,1,2 0.02903 Ha,1,2 accepted

The integrated profile improved the quality of the recommendersystem.

J. Magalhaes, C. Souza, P. Silva, E. Costa and J. Fechine SRS – UMAP – 2012 9

IntroductionOur Purpose

Evaluation and ResultsConclusion

Evaluation

With new users:

t3 with Ha,2,1: integrated is greater than lattes

t4 with Ha,2,2: integrated is greater than linkedin

Table: Comparison between the three strategies using the Student’s t-test(α = 0.05) with the new users.

T-test Alternative p-value MeaningHypothesis

t3 Ha,2,1 0.03485 Ha,2,1 accepted

t4 Ha,2,2 0.04208 Ha,2,2 accepted

The integrated profile reduced the cold-start problem.

J. Magalhaes, C. Souza, P. Silva, E. Costa and J. Fechine SRS – UMAP – 2012 10

IntroductionOur Purpose

Evaluation and ResultsConclusion

Conclusion and Future Work

We confirm the superiority of the integrated profile;

But the results can be better.

Planning and execution of an experiment with:

More volunteers;More data sources.

The recommender system as support to learners in an IntelligentTutoring System.

J. Magalhaes, C. Souza, P. Silva, E. Costa and J. Fechine SRS – UMAP – 2012 11

IntroductionOur Purpose

Evaluation and ResultsConclusion

Thanks!!

J. Magalhaes, C. Souza, P. Silva, E. Costa and J. Fechine SRS – UMAP – 2012 12

IntroductionOur Purpose

Evaluation and ResultsConclusion

For more information visit the TIPS Group: http://tip.ic.ufal.br/site/

You are welcome to Maceio!

J. Magalhaes, C. Souza, P. Silva, E. Costa and J. Fechine SRS – UMAP – 2012 13