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Personalizing recommendation diversity based on user personality
Wen Wu
*
,
Li Chen
, Yu Zhao
*
Corresponding author for this work
Department of Computer Science
Research output
:
Contribution to journal
›
Journal article
›
peer-review
68
Citations (Scopus)
Overview
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Dive into the research topics of 'Personalizing recommendation diversity based on user personality'. Together they form a unique fingerprint.
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Keyphrases
User Diversity
100%
Recommendation Diversity
100%
User Personality
100%
Diversity-oriented
100%
Recommender Systems
50%
Personal Factors
50%
User Behavior
50%
Cold-start Problem
50%
New User
50%
Diversity Preference
50%
Recommendation Accuracy
50%
User Needs
50%
Re-ranking
50%
Ranking Approach
50%
Collaborative Filtering Recommendation
50%
Recommendation List
50%
Generalized Dynamics
50%
Personalized Diversity
50%
Dynamic Personality
50%
Computer Science
Experimental Result
100%
Recommender System
100%
Collaborative Filtering
100%
User Behavior
100%
Cold Start Problem
100%
Recommendation Accuracy
100%
Individual User
100%
Oriented Method
100%
Personal Factor
100%
Engineering
Cold Start
100%
Experimental Result
50%
Limitations
50%
Filtration
50%
Metrics
50%
Individual User
50%