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.