Recommender systems that include some reliability measure of their predictions tend to be more conservative in forecasting, due to their constraint to preserve reliability. This leads to a significant drop in the coverage and novelty that these systems can provide. In this paper, we propose the inclusion of a new term in the learning process of matrix factorization-based recommender systems, called recklessness, which enables the control of the risk level desired when making decisions about the reliability of a prediction. Experimental results demonstrate that recklessness not only allows for risk regulation but also improves the quantity and quality of predictions provided by the recommender system.

翻译：在推荐系统中，若包含某种预测可靠性度量，则因需保持可靠性约束，其预测结果往往趋于保守。这导致系统所能提供的覆盖范围和新颖性显著下降。本文提出在基于矩阵分解的推荐系统学习过程中引入一个新术语——鲁莽性，它能够控制对预测可靠性做出决策时所需的冒险程度。实验结果表明，鲁莽性不仅实现了风险调节，还提升了推荐系统提供预测的数量与质量。