Personalized top-N recommender algorithms have been investigated widely in decades. The core task of different recommender algorithms is to estimate user-item preference scores and then to suggest, for each user, top-N items that have high preference scores. However, little attention was paid to the recommendation of items with low preference scores. In this work, we use the bayesian estimation theory to build the relationship between the estimated preference scores and the actual recommendation accuracy. We then propose a novel metric RNR (Recall-to-Noise Ratio) to recommend items with low estimated preference scores. An interesting counterintuitive phenomenon is found that user-item links with low preference scores may achieve higher accuracy than high preference score ones in the recommendation. Based on RNR, we design a generic framework that could recommend user-item links that have low preference scores without the loss of recommendation accuracy. The effectiveness of the proposed framework is illustrated by both theoretical analysis and empirical experiments.

几十年来，个性化的 top-N 推荐算法得到了广泛的研究。不同推荐算法的核心任务是估计用户-项目偏好分数，然后为每个用户推荐具有高偏好分数的前 N ​​个项目。然而，很少关注偏好分数较低的项目的推荐。在这项工作中，我们使用贝叶斯估计理论来建立估计偏好分数与实际推荐准确度之间的关系。然后，我们提出了一种度量RNR（ř eCall的TO- Ñ瓦兹řatio) 以推荐具有较低估计偏好分数的项目。一个有趣的违反直觉的现象被发现，在推荐中，具有低偏好分数的用户-项目链接可能比高偏好分数的链接获得更高的准确性。基于 RNR，我们设计了一个通用框架，可以在不损失推荐准确性的情况下推荐具有低偏好分数的用户-项目链接。理论分析和实证实验都说明了所提出框架的有效性。