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Personality and Recommender Systems
Marko Tkalcic
*
,
Li Chen
*
Corresponding author for this work
Department of Computer Science
Research output
:
Chapter in book/report/conference proceeding
›
Chapter
›
peer-review
116
Citations (Scopus)
Overview
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Dive into the research topics of 'Personality and Recommender Systems'. Together they form a unique fingerprint.
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Psychology
Conscientiousness
100%
Five-Factor Model
100%
Mobile Phone
100%
Neuroticism
100%
Extraversion
100%
Individual Differences
100%
Phone Call
100%
Learning Style
100%
Agreeableness
100%
Keyphrases
Recommender Systems
100%
Personality System
100%
Conscientiousness
25%
Machine Learning Techniques
25%
User Preference
25%
Numerical Value
25%
User Behavior
25%
Cold-start Problem
25%
Personality Factors
25%
Music Preference
25%
Cross-domain
25%
Neuroticism
25%
Extraversion
25%
User Characteristics
25%
Behavior Learning
25%
Personality Characteristics
25%
Learning Styles
25%
Five-factor Model
25%
Feature Extracting
25%
Agreeableness
25%
Personality Models
25%
Social Media Stream
25%
Social Media Behavior
25%
Group Recommendation
25%
System Experiment
25%
Diverse Recommendations
25%
Call Logs
25%
Mobile Phone Calls
25%
Computer Science
Recommender System
100%
User Preference
25%
User Behavior
25%
Cold Start Problem
25%
Personality Factor
25%
Conscientiousness
25%
Extracted Feature
25%
Machine Learning Technique
25%
Learning Style
25%
Numerical Value
25%