Building Systems to{Capture | Measure | Use}Emotions and Personality@neal_lathiacambridge computer lab, uk#umap2013 rome, ...
----- disclaimer -----
affective computing:“relates to, arises from, or influences emotions” …user modeling:“systems that adapt […] based on an i...
affective computing:“relates to, arises from, or influences emotions” …user modeling:“systems that adapt […] based on an i...
1. what role do emotions play in personalised, user-model based systems?2. can we measure emotion accurately?3. what can w...
1. what role do emotions play in personalised, user-model based systems?
recommender systemsPhD: “does our research match deployed reality?”(user modeling)
research: (mostly) driven by off-line studiespractice: (definitely) driven for recurring interaction
research: simulate recurring interaction with one ofthree surveys. note: one survey did not change itsrecommendations.
result (expected): people prefer thoserecommendations that are diverse and change overtime.research: lets build this into ...
result (unexpected):“there is a bug in your system.. it sux […]”“i used to work for [famous recsys company];building a rec...
lesson?personalized systems and users emotions willnever be mutually exclusive… or even uni-directional (more ahead)
2. can we measure emotion accurately?what are we measuring? how do we measure it?
“people might be said to have an implicit theory ofemotions […] laymens cognitive representation ofemotion is presumably i...
“...investigators who have factor analyzed self-reported affective states have typically concludedthat there are between s...
users are being asked to perform two separatetasks: (1) estimate, based on self-knowledge, and(2) translate onto a rating ...
2. can we measure emotion accurately solicit quick,meaningful representations of emotions?
angry anxious lonelyrelaxedenthusiasticcalm
early results: consistent, highly correlated usage ofthe affect grid.but.. not everyone is using it correctly;predictions ...
an earlier study (22 participants, 4 weeks)experimented with the question “when do we askusers how they feel?”.
N. Lathia, K. Rachuri, C. Mascolo, P. Rentfrow. Contextual Dissonance: Design Bias inSensor-Based Experience Sampling Meth...
lesson?the emotion and behaviours you measure ~ howyou measure them
3. what can we compute using (representations of)emotion and personality?
affect classification: how is the user feeling?rating prediction: what rating would the user give?
affect classification ~ rating predictionfun research task, but how useful?what about recommending stuff?
users itemspreferencessearching for “context” ...
“...the specifics of the context surrounding people’sday-to-day living are much more subtle, fluid andidiosyncratic than t...
users itemspreferencestraitstate
userspreferences?statetraitpreferences?items
userspreferences?statetraitpreferences?itemsdata we have (not Emotion Sense!):personality scores, music listening histories
early results: augmenting music recommendationalgorithms with personality data improves averageranking by more than 10%but...
1. what role do emotions play in personalised, user-model based systems?2. can we measure emotion accurately?3. what can w...
References1. R. W. Picard. Affective Computing. M.I.T Media Laboratory Perceptual ComputingSection Technical Report No. 32...
Building Systems to Capture, Measure, and Use Emotions and Personality
Building Systems to Capture, Measure, and Use Emotions and Personality
Building Systems to Capture, Measure, and Use Emotions and Personality
Building Systems to Capture, Measure, and Use Emotions and Personality
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Building Systems to Capture, Measure, and Use Emotions and Personality

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Building Systems to Capture, Measure, and Use Emotions and Personality

  1. 1. Building Systems to{Capture | Measure | Use}Emotions and Personality@neal_lathiacambridge computer lab, uk#umap2013 rome, italy. june 2013
  2. 2. ----- disclaimer -----
  3. 3. affective computing:“relates to, arises from, or influences emotions” …user modeling:“systems that adapt […] based on an internalrepresentation of the user”
  4. 4. affective computing:“relates to, arises from, or influences emotions” …user modeling:“systems that adapt […] based on an internalrepresentation of the user”
  5. 5. 1. what role do emotions play in personalised, user-model based systems?2. can we measure emotion accurately?3. what can we compute using (representations of)emotion and personality?
  6. 6. 1. what role do emotions play in personalised, user-model based systems?
  7. 7. recommender systemsPhD: “does our research match deployed reality?”(user modeling)
  8. 8. research: (mostly) driven by off-line studiespractice: (definitely) driven for recurring interaction
  9. 9. research: simulate recurring interaction with one ofthree surveys. note: one survey did not change itsrecommendations.
  10. 10. result (expected): people prefer thoserecommendations that are diverse and change overtime.research: lets build this into recsys & see how itaffects quantitative metrics.N. Lathia, S. Hailes, L. Capra, X. Amatriain. “Temporal Diversity inRecommender Systems.” in ACM SIGIR 2010, Geneva, Switzerland.
  11. 11. result (unexpected):“there is a bug in your system.. it sux […]”“i used to work for [famous recsys company];building a recommender system is not hard...”– angry, frustrated; low ratings may encode“punishing” a system (beyond preference)
  12. 12. lesson?personalized systems and users emotions willnever be mutually exclusive… or even uni-directional (more ahead)
  13. 13. 2. can we measure emotion accurately?what are we measuring? how do we measure it?
  14. 14. “people might be said to have an implicit theory ofemotions […] laymens cognitive representation ofemotion is presumably implicit in the sense that fewif any could explicitly state their conceptualframework...”- Russel (1980)J. A. Russel. A Circumplex Model of Affect. Journal of Personality and SocialPsychology. Vol 39, No. 6. 1980.
  15. 15. “...investigators who have factor analyzed self-reported affective states have typically concludedthat there are between six and twelve independentmonopolar factors of affect...”- Russel (1980)J. A. Russel. A Circumplex Model of Affect. Journal of Personality and SocialPsychology. Vol 39, No. 6. 1980.
  16. 16. users are being asked to perform two separatetasks: (1) estimate, based on self-knowledge, and(2) translate onto a rating scale... and thissometimes causes problems** X. Amatriain, J. Pujol, N. Oliver. “I Like It.. I Like It Not... Measuring Users RatingsNoise in Recommender Systems.” In UMAP 2009, Trento, Italy
  17. 17. 2. can we measure emotion accurately solicit quick,meaningful representations of emotions?
  18. 18. angry anxious lonelyrelaxedenthusiasticcalm
  19. 19. early results: consistent, highly correlated usage ofthe affect grid.but.. not everyone is using it correctly;predictions of r (adjective) ~ (x, y, ….) ongoingand, more importantly...
  20. 20. an earlier study (22 participants, 4 weeks)experimented with the question “when do we askusers how they feel?”.
  21. 21. N. Lathia, K. Rachuri, C. Mascolo, P. Rentfrow. Contextual Dissonance: Design Bias inSensor-Based Experience Sampling Methods. To appear, ACM Ubicomp 2013, Zurich,Switzerland.[…] negative affect ratings [...] were significantlydifferent from one another with at least 90%confidence […] we observe that our designparameters influence the outcome...
  22. 22. lesson?the emotion and behaviours you measure ~ howyou measure them
  23. 23. 3. what can we compute using (representations of)emotion and personality?
  24. 24. affect classification: how is the user feeling?rating prediction: what rating would the user give?
  25. 25. affect classification ~ rating predictionfun research task, but how useful?what about recommending stuff?
  26. 26. users itemspreferencessearching for “context” ...
  27. 27. “...the specifics of the context surrounding people’sday-to-day living are much more subtle, fluid andidiosyncratic than theories of context have led us tobelieve...”- Y. RogersY. Rogers. Moving on from Weisers Vision of Calm Computing: Engaging UbicompExperiences. In ACM Ubicomp 2006. Orange County, USA
  28. 28. users itemspreferencestraitstate
  29. 29. userspreferences?statetraitpreferences?items
  30. 30. userspreferences?statetraitpreferences?itemsdata we have (not Emotion Sense!):personality scores, music listening histories
  31. 31. early results: augmenting music recommendationalgorithms with personality data improves averageranking by more than 10%but.. this still doesnt seem to outperform just“working harder” with the rating data
  32. 32. 1. what role do emotions play in personalised, user-model based systems?2. can we measure emotion accurately?3. what can we compute using (representations of)emotion and personality?
  33. 33. References1. R. W. Picard. Affective Computing. M.I.T Media Laboratory Perceptual ComputingSection Technical Report No. 3212. N. Lathia, S. Hailes, L. Capra, X. Amatriain. Temporal Diversity in RecommenderSystems. In ACM SIGIR 2010, Geneva, Switzerland.3. J. A. Russel. A Circumplex Model of Affect. Journal of Personality and SocialPsychology. Vol 39, No. 6. 1980.4. X. Amatriain, J. Pujol, N. Oliver. “I Like It.. I Like It Not... Measuring Users RatingsNoise in Recommender Systems.” In UMAP 2009, Trento, Italy5. N. Lathia, K. Rachuri, C. Mascolo, P. Rentfrow. Contextual Dissonance: DesignBias in Sensor-Enhanced Experience Sampling Methods. To appear, ACM Ubicomp2013. Zurich, Switzerland.6. Y. Rogers. Moving on from Weisers Vision of Calm Computing: EngagingUbicomp Experiences. In ACM Ubicomp 2006. Orange County, USA

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