Introduction                                            Our Purpose                                  Evaluation and Result...
Introduction                                            Our Purpose                                  Evaluation and Result...
Introduction                                            Our Purpose                                  Evaluation and Result...
Introduction                                            Our Purpose                                  Evaluation and Result...
Introduction                                            Our Purpose                                  Evaluation and Result...
Introduction                                            Our Purpose                                  Evaluation and Result...
Introduction                                            Our Purpose                                  Evaluation and Result...
Introduction                                            Our Purpose                                  Evaluation and Result...
Introduction                                            Our Purpose                                  Evaluation and Result...
Introduction                                            Our Purpose                                  Evaluation and Result...
Introduction                                            Our Purpose                                  Evaluation and Result...
Introduction                                            Our Purpose                                  Evaluation and Result...
Introduction                                            Our Purpose                                  Evaluation and Result...
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Improving a Recommender System Through Integration of User Profiles: a Semantic Approach

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The users are present in multiple social networks/virtual communities and each one can be considered as a source of information about this user. In face to this question it is important a mechanism to integrate the user profiles. Through the integration of user profiles it is possible identifier more accurately their interests analyzing other data sources that they are present, possible reducing the cold-start problem. In this context, we present a semantic approach to help integrate data from multiple sources, for the construction and maintenance of user profiles that will be used to improve the quality of a recommender system. To integrate data from multiple sources, we defined a heuristic that quantifies the importance of each data source for a given user. To validate our approach, we perform a case study, where the solution was coupled into a recommender system of papers focused in Software Engineering domain. The user profiles were built extracting their information from the Brazilian Curriculum Vitae database named CV-Lattes, an academic platform, and Linkedin, a business network. We compared the quality of the recommendation based on the profiles integrated and non-integrated. The results show the superior quality of the recommendation based on integrated profile.

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Improving a Recommender System Through Integration of User Profiles: a Semantic Approach

  1. 1. Introduction Our Purpose Evaluation and Results Conclusion Improving a Recommender System Through Integration of User Profiles: a Semantic Approach Jonathas Magalh˜es, Cleyton Souza, Priscylla Silva, a Evandro Costa and Joseana Fechine TIPS Group Federal University of Campina Grande, Brazil Federal University of Alagoas, BrazilJ. Magalh˜es, C. Souza, P. Silva, E. Costa and J. Fechine a SRS – UMAP – 2012 1
  2. 2. Introduction Our Purpose Evaluation and Results ConclusionIntroductionJ. Magalh˜es, C. Souza, P. Silva, E. Costa and J. Fechine a SRS – UMAP – 2012 2
  3. 3. Introduction Our Purpose Evaluation and Results ConclusionIntroductionOur purposeJ. Magalh˜es, C. Souza, P. Silva, E. Costa and J. Fechine a SRS – UMAP – 2012 3
  4. 4. Introduction Our Purpose Evaluation and Results ConclusionIntroductionQuestions: Q1 : Will the profiles integration improve the quality of recommendation of a recommender system? Q2 :Will the profiles integration reduce the cold-start problem in a recommender system?J. Magalh˜es, C. Souza, P. Silva, E. Costa and J. Fechine a SRS – UMAP – 2012 4
  5. 5. Introduction Our Purpose Evaluation and Results ConclusionThe User Profiles Integration s s |S| s s pu = integration(pu1 , pu2 , ..., pu ) = pu · au , (1) s∈S swhere auj : Represents the importance of the data source sj to the integrated profile; Is computed by the activity of the user u in the source data sj : |Iu,s |−1 s tnow − t|Iu,s | + j=1 (tj+1 − tj ) au = . (2) |Iu,s | + 1J. Magalh˜es, C. Souza, P. Silva, E. Costa and J. Fechine a SRS – UMAP – 2012 5
  6. 6. Introduction Our Purpose Evaluation and Results ConclusionEvaluation The case study focused in the Software Engineering Domain, using an domain ontology; The terms of ontology were learn using 100 papers for each concept; In this case study participated ten researches, where five were new users; Their profiles were built using three different strategies: Using the Lattes CV ; Using the Linkedin; The integration of both.J. Magalh˜es, C. Souza, P. Silva, E. Costa and J. Fechine a SRS – UMAP – 2012 6
  7. 7. Introduction Our Purpose Evaluation and Results ConclusionEvaluation We coupled the three different strategies in a Recommender System of papers; The papers (40,855) were loaded from the digital library CiteerSeeX; We recommended 15 papers, five for each strategy; The users evaluated the recommended papers in a scale from 0 to 4; We compare the strategies using the metric Normalized Discounted Cumulative Gain (nDCG).J. Magalh˜es, C. Souza, P. Silva, E. Costa and J. Fechine a SRS – UMAP – 2012 7
  8. 8. Introduction Our Purpose Evaluation and Results ConclusionResultsWith all users: Figure: Comparing the three strategies to built the user profile.J. Magalh˜es, C. Souza, P. Silva, E. Costa and J. Fechine a SRS – UMAP – 2012 8
  9. 9. Introduction Our Purpose Evaluation and Results ConclusionEvaluationWith all users: t1 with Ha,1,1 : integrated is greater than lattes t2 with Ha,1,2 : integrated is greater than linkedinTable: Comparison between the three strategies using the Student’s t-test(α = 0.05) with all users. T-test Alternative p-value Meaning Hypothesis t1 Ha,1,1 0.03152 Ha,1,1 accepted t2 Ha,1,2 0.02903 Ha,1,2 acceptedThe integrated profile improved the quality of the recommendersystem.J. Magalh˜es, C. Souza, P. Silva, E. Costa and J. Fechine a SRS – UMAP – 2012 9
  10. 10. Introduction Our Purpose Evaluation and Results ConclusionEvaluationWith new users: t3 with Ha,2,1 : integrated is greater than lattes t4 with Ha,2,2 : integrated is greater than linkedinTable: Comparison between the three strategies using the Student’s t-test(α = 0.05) with the new users. T-test Alternative p-value Meaning Hypothesis t3 Ha,2,1 0.03485 Ha,2,1 accepted t4 Ha,2,2 0.04208 Ha,2,2 acceptedThe integrated profile reduced the cold-start problem.J. Magalh˜es, C. Souza, P. Silva, E. Costa and J. Fechine a SRS – UMAP – 2012 10
  11. 11. Introduction Our Purpose Evaluation and Results ConclusionConclusion 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 Intelligent Tutoring System.J. Magalh˜es, C. Souza, P. Silva, E. Costa and J. Fechine a SRS – UMAP – 2012 11
  12. 12. Introduction Our Purpose Evaluation and Results Conclusion Thanks!!J. Magalh˜es, C. Souza, P. Silva, E. Costa and J. Fechine a SRS – UMAP – 2012 12
  13. 13. Introduction Our Purpose Evaluation and Results ConclusionFor more information visit the TIPS Group: http://tip.ic.ufal.br/site/ You are welcome to Macei´! oJ. Magalh˜es, C. Souza, P. Silva, E. Costa and J. Fechine a SRS – UMAP – 2012 13

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