C4.5: programs for machine learning
C4.5: programs for machine learning
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Is seeing believing?: how recommender system interfaces affect users' opinions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Improving the Quality of the Personalized Electronic Program Guide
User Modeling and User-Adapted Interaction
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Trust-inspiring explanation interfaces for recommender systems
Knowledge-Based Systems
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Explanations of recommendations
Proceedings of the 2007 ACM conference on Recommender systems
The effects of transparency on trust in and acceptance of a content-based art recommender
User Modeling and User-Adapted Interaction
A Survey of Accuracy Evaluation Metrics of Recommendation Tasks
The Journal of Machine Learning Research
Temporal diversity in recommender systems
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Recommender Systems Handbook
A user-centric evaluation framework for recommender systems
Proceedings of the fifth ACM conference on Recommender systems
A new criteria for selecting neighborhood in memory-based recommender systems
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
Recommender systems: from algorithms to user experience
User Modeling and User-Adapted Interaction
Explaining the user experience of recommender systems
User Modeling and User-Adapted Interaction
Evaluating the effectiveness of explanations for recommender systems
User Modeling and User-Adapted Interaction
Explaining neighborhood-based recommendations
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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Recommender systems suggest new items to users to try or buy based on their previous preferences or behavior. Many times the information used to recommend these items is limited. An explanation such as"I believe you will like this item, but I do not have enough information to be fully confident about it." may mitigate the issue, but can also damage user trust because it alerts users to the fact that the system might be wrong. The findings in this paper suggest that there is a way of modelling recommendation confidence that is related to accuracy (MAE, RMSE and NDCG) and user rating behaviour (rated vs unrated items). In particular, it was found that unrated items have lower confidence compared to the entire item set - highlighting the importance of explanations for novel but risky recommendations.