Recommender systems are intrinsically tied to a reliability/coverage dilemma: The more reliable we desire the forecasts, the more conservative the decision will be and thus, the fewer items will be recommended. This leads to a significant drop in the novelty of these systems, since instead of recommending uncertain unusual items, they focus on predicting items with guaranteed success. In this paper, we propose the inclusion of a new term in the learning process of matrix factorization-based recommender systems, called recklessness, that takes into account the variance of the output probability distribution of the predicted ratings. In this way, gauging this recklessness measure we can force more spiky output distribution, enabling 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：推荐系统本质上受限于可靠性/覆盖范围的两难困境：预测结果的可靠性要求越高，决策就越保守，因此推荐的项目数量就越少。这导致推荐系统新颖性显著下降，因为系统不再推荐不确定的罕见项目，而是专注于预测有保证成功的项目。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，该术语考虑了预测评分输出概率分布的方差。通过衡量这一鲁莽性指标，我们可以使输出分布更加尖锐，从而在判断预测可靠性时控制所需的风险水平。实验结果表明，鲁莽性不仅能够调节风险，还能提升推荐系统提供预测的数量和质量。