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Emotion-aware recommender systems Master’s thesis 2015-2016 SupervisorMentorStudent Katrien VerbertKarsten SeippJeroen Reinenbergh.

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Presentation on theme: "Emotion-aware recommender systems Master’s thesis 2015-2016 SupervisorMentorStudent Katrien VerbertKarsten SeippJeroen Reinenbergh."— Presentation transcript:

1 Emotion-aware recommender systems Master’s thesis 2015-2016 SupervisorMentorStudent Katrien VerbertKarsten SeippJeroen Reinenbergh

2 Research topics Emotion recognition o Automatic o Mobile o Unobtrusive Recommender system o Emotions as input and/or feedback o Music recommendation

3 Emotion recognition – Literature* * Bibliographic references can be found at https://jeroenrthesis.wordpress.com/

4 Emotion recognition – Literature* * Bibliographic references can be found at https://jeroenrthesis.wordpress.com/

5 Emotion recognition – Signals Subjective o Surveys o Self-Assessment Manikin o... Behavioural o Facial expressions o Body gestures o …

6 Emotion recognition – Signals Physiological o Brain activity o Blood volume pulse o Blood pressure o Heart rate o Skin conductance o Skin temperature o Respiration o …

7 Emotion recognition – Sensors Muse headband o Brain activity o Head movement o Status indicators o Artifacts Empatica E4 wristband o Blood volume pulse o Skin conductance o Temperature o Movement

8 Emotion recognition – Difficulties o Unobtrusiveness  accuracy o Discrete  dimensional

9 Android app – Recording sensor data 

10 Android app – Visualizing sensor data EEGHorseshoe

11 Android app – Visualizing sensor data ArtifactsAccelerometer

12 Android app – Demo

13 Android app – Analyzing sensor data EEGHorseshoe

14 Android app – Difficulties o Android framework o Sensor APIs

15 Schedule – First semester o September - October Literature Android basics o November Data extraction Data visualization o December Discrete emotion detection Feature extraction Building classifier(s) Inclusion of Empatica E4 wristband data

16 Schedule – Second semester o February - March Iterative evaluation and refinement Real-time detection o April Mobile detection Inclusion of recommender system o May Small party Writing o June Big party

17 Statistics 9 blog posts 3 blog comments 122 papers read/skimmed 290 hours of work o 65% design/implementation o 25% reading o 4% writing o 4% reflection o 2% meetings ≥ 14/20 expected

18 Q & A


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