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 causes a detriment to the predictive capability of the system, as it is only able to estimate potential interest in items for which there is a consensus in their evaluation, rather than being able to estimate potential interest in any item. 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：推荐系统本质上面临着一个可靠性/覆盖率的困境：我们越期望预测可靠，决策就会越保守，从而推荐的物品数量就越少。这损害了系统的预测能力，因为系统只能估计那些评价存在共识的物品的潜在兴趣，而无法估计任何物品的潜在兴趣。在本文中，我们提出在基于矩阵分解的推荐系统的学习过程中引入一个新术语，称为鲁莽性，该术语考虑了预测评分输出概率分布的方差。通过这种方式，衡量这一鲁莽性度量，我们可以强制输出分布更加尖锐，从而能够在决定预测可靠性时控制所需的风险水平。实验结果表明，鲁莽性不仅允许风险调节，还能提高推荐系统提供的预测数量和质量。