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1. Inferring Eudaimonia and Hedonia from Digital Traces

2. Personality, Emotions, and Group Dynamics

3. Conclusions

4. Predicting Music Relistening Behavior Using the ACT-R Framework

8. Complementing Behavioural Modeling with Cognitive Modeling for Better Recommendations

10. Explanations for Groups

11. Further Choice Scenarios

12. Biases in Group Decisions

13. Personality, Emotions, and Group Dynamics

14. Handling Preferences

15. Group Recommender Applications

16. Decision Tasks and Basic Algorithms

17. Evaluating Group Recommender Systems

18. Algorithms for Group Recommendation

20. Affective and Personality Corpora

21. Introduction to Emotions and Personality in Personalized Systems

25. Using Instagram Picture Features to Predict Users’ Personality

26. Conclusions

27. Personality Correlates for Digital Concert Program Notes

28. On the Influence of User Characteristics on Music Recommendation Algorithms

32. Personality-Based Active Learning for Collaborative Filtering Recommender Systems

33. Impact of Implicit and Explicit Affective Labeling on a Recommender System’s Performance

35. Prediction of Hedonic and Eudaimonic Characteristics from User Interactions

47. Factors influencing privacy concern for explanations of group recommendation

48. Exploring User Concerns about Disclosing Location and Emotion Information in Group Recommendations

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