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Addressing the New User Problem with a Personality Based User Similarity Measure

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  • 1. Addressing the New User Problem with a Personality Based User Similarity Measure Marko Tkalčič, Matevž Kunaver, Andrej Košir and Jurij TasičUniverza v Ljubljani ..: Fakulteta za elektrotehniko:..[LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:..
  • 2. Univerza v Ljubljani ..: Fakulteta za elektrotehniko:.. [LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:.. Overview Area overview Problem statement Proposed solution Methodology Results Conclusion
  • 3. Univerza v Ljubljani ..: Fakulteta za elektrotehniko:..[LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:..Collaborative filtering recommender systems Which film should I watch tonight?
  • 4. Univerza v Ljubljani ..: Fakulteta za elektrotehniko:..[LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:..Collaborative filtering recommender systems Which film should I watch tonight? Users space
  • 5. Univerza v Ljubljani ..: Fakulteta za elektrotehniko:..[LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:..Collaborative filtering recommender systems Which film should User similarity measure I watch tonight? Users space
  • 6. Univerza v Ljubljani ..: Fakulteta za elektrotehniko:..[LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:..Collaborative filtering recommender systems Which film should User similarity measure I watch tonight? Users space
  • 7. Univerza v Ljubljani ..: Fakulteta za elektrotehniko:.. [LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:.. User Similarity Measure (USM) Baseline: rating-based USM – Based on overlapping ratings – Estimation of rating for observed user Few ratings (m)NEW USER PROBLEM (NUP)  COLD START PROBLEM (CSP)
  • 8. Univerza v Ljubljani ..: Fakulteta za elektrotehniko:.. [LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:.. Problem statementRS performance Number of overlapping ratings m
  • 9. Univerza v Ljubljani ..: Fakulteta za elektrotehniko:.. [LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:.. Problem statement1. How to minimize the CSP?RS performance Number of overlapping ratings m
  • 10. Univerza v Ljubljani ..: Fakulteta za elektrotehniko:.. [LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:.. Problem statement1. How to minimize the NUP?2. Where is the CSP boundary? ?RS performance Number of overlapping ratings m
  • 11. Univerza v Ljubljani ..: Fakulteta za elektrotehniko:.. [LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:.. Proposed solution1. How to minimize the CSP? – Personality based USM – Why?  users with similar personalities tend to prefer similar items (presumption) – + No need for ratings – - Need for a startup questionnaire to assess personality2. Where is the CSP boundary? – Statistical test: • H0: avg(Fs) = avg(Fs+1 )
  • 12. Univerza v Ljubljani ..: Fakulteta za elektrotehniko:.. [LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:.. What is personality? ... personality refers to the enduring patterns of thought, feeling, motivation and behaviour that are expressed in different circumstances [Westen1999] Five Factor Model (FFM) FFM is a hierarchical organization of personality traits in terms of five basic dimensions: – extraversion (E), – agreeableness (A), – conscientiousness (C), – neuroticism (N) – openness (O) These are the most important ways in which the individuals differ in their enduring emotional, interpersonal, experiential, attitudinal and motivational styles
  • 13. Univerza v Ljubljani ..: Fakulteta za elektrotehniko:.. [LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:.. Methodology Recommender system‘s task: estimate unrated items Offline Compare performance – Baseline USM at different stages s (= CSP simulation) – Personality based USM Each user u is modeled with a five tuple b=(b1, b2, b3, b4, b5) FFM acquisition with IPIP questionnaire (50 questions) Distance between 2 users ui and uj 7 neighbors Rating estimation Evaluation: – confusion matrix for ratings estimations F measure – T test
  • 14. Univerza v Ljubljani ..: Fakulteta za elektrotehniko:.. [LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:.. Dataset LDOS-PerAff-1: – I = 52 users – J = 70 items (images from IAPS dataset)
  • 15. Univerza v Ljubljani ..: Fakulteta za elektrotehniko:..[LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:..Results 1: p-values of USM comparison
  • 16. Univerza v Ljubljani ..: Fakulteta za elektrotehniko:..[LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:..Results 2: p- values for CSP boundary
  • 17. Univerza v Ljubljani ..: Fakulteta za elektrotehniko:.. [LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:.. Conclusions Proposed USM performs better in under cold start circumstances – i.e. Personality accounts for between-users variance in entertainment apps Proposed USM is equivalent under normal (non-CS) conditions Drawbacks: – Need for FFM assessment – Ethical/privacy issues – Evaluated on this dataset only
  • 18. Thank youUniverza v Ljubljani ..: Fakulteta za elektrotehniko:..[LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:..
  • 19. Univerza v Ljubljani ..: Fakulteta za elektrotehniko:.. [LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:.. IPIP questionnaire Freely available
  • 20. Univerza v Ljubljani ..: Fakulteta za elektrotehniko:..[LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:..Results: F measure boxplots