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An explanation-based approach for experiment reproducibility in recommender systems
Nikolaos Polatidis
, Antonios Papaleonidas
, Elias Pimenidis
, Lazaros Iliadis
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peer-review
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Keyphrases
Recommender Systems
100%
Experiment Reproducibility
100%
Collaborative Filtering
50%
Root Mean Square Error
50%
Offline Evaluation
50%
Recommendation Evaluation
50%
Mean Absolute Error
50%
Rating Prediction
50%
Running Experiment
50%
Top-N Recommendation
50%
Recommendation Approach
50%
Recommendation Algorithm
50%
Fuzzy Recommendation
50%
Accuracy Metrics
50%
Ratings Precision
50%
Filter Bubble
50%
Computer Science
Recommender Systems
100%
Reproducibility
100%
Collaborative Filtering
50%
Mean Absolute Error
50%
Recommendation Algorithm
50%
Root Mean Squared Error
50%