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User Bias in Beyond-Accuracy Measurement of Recommendation Algorithms
Ningxia Wang
,
Li Chen
Department of Computer Science
Research output
:
Chapter in book/report/conference proceeding
›
Conference proceeding
›
peer-review
31
Citations (Scopus)
Overview
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Keyphrases
Measurement Accuracy
100%
Recommendation Algorithm
100%
Beyond-accuracy
100%
User Bias
100%
Usage Patterns
40%
User Characteristics
40%
User Groups
20%
Recommender Systems
20%
Large-scale Survey
20%
Performance Objectives
20%
Serendipity
20%
Algorithm Accuracy
20%
User Ratings
20%
Survey Datasets
20%
Personalization
20%
Accuracy Performance
20%
Big Five Traits
20%
Unexpectedness
20%
Filter Bubble Phenomenon
20%
System Optimization
20%
Commercial Platform
20%
Algorithmic Bias
20%
Accuracy Metrics
20%
Computer Science
User Behavior
100%
Recommendation Algorithm
100%
Behavior Pattern
100%
Unfairness
100%
Recommender System
50%
Algorithmic Bias
50%
Engineering
Metrics
100%