The main goal of a Recommender System is to suggest relevant items to users, although other utility dimensions – such as diversity, novelty, confidence, possibility of providing explanations – are often considered. In this work, we investigate about confidence but from the perspective of the system: what is the confidence a system has on its own recommendations; more specifically, we focus on different methods to embed awareness into the recommendation algorithms about deciding whether an item should be suggested. Sometimes it is better not to recommend than fail because failure can decrease user confidence in the system. In this way, we hypothesise the system should only show the more reliable suggestions, hence, increasing the performance of such recommendations, at the expense of, presumably, reducing the number of potential recommendations. Different from other works in the literature, our approaches do not exploit or analyse the input data but intrinsic aspects of the recommendation algorithms or of the components used during prediction are considered. We propose a taxonomy of techniques that can be applied to some families of recommender systems allowing to include mechanisms to decide if a recommendation should be generated. In particular, we exploit the uncertainty in the prediction score for a probabilistic matrix factorisation algorithm and the family of nearest-neighbour algorithms, the support of the prediction score for nearest-neighbour algorithms, and a method independent of the algorithm. We study how the performance of a recommendation algorithm evolves when it decides not to recommend in some situations. If the decision of avoiding a recommendation is sensible – i.e., not random but related to the information available to the system about the target user or item –, the performance is expected to improve at the expense of other quality dimensions such as coverage, novelty, or diversity. This balance is critical, since it is possible to achieve a very high precision recommending only one item to a unique user, which would not be a very useful recommender. Because of this, on the one hand, we explore some techniques to combine precision and coverage metrics, an open problem in the area. On the other hand, a family of metrics (correctness) based on the assumption that it is better to avoid a recommendation rather than providing a bad recommendation is proposed herein. In summary, the contributions of this paper are twofold: a taxonomy of techniques that can be applied to some families of recommender systems allowing to include mechanisms to decide if a recommendation should be generated, and a first exploration to the combination of evaluation metrics, mostly focused on measures for precision and coverage. Empiric results show that large precision improvements are obtained when using these approaches at the expense of user and item coverage and with varying levels of novelty and diversity.

中文翻译：

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利用决策感知推荐系统中的推荐置信度

推荐系统的主要目标是向用户推荐相关项目，但通常会考虑其他效用维度——例如多样性、新颖性、置信度、提供解释的可能性。在这项工作中，我们从系统的角度研究置信度：系统对其自身推荐的置信度是多少？更具体地说，我们专注于将意识嵌入到推荐算法中的不同方法，以决定是否应该建议一个项目。有时不推荐比失败更好，因为失败会降低用户对系统的信心。通过这种方式，我们假设系统应该只显示更可靠的建议，因此，提高此类建议的性能，可能会以减少潜在建议的数量为代价。与文献中的其他作品不同，我们的方法不利用或分析输入数据，而是考虑了推荐算法或预测过程中使用的组件的内在方面。我们提出了一种技术分类法，可应用于某些推荐系统系列，允许包括决定是否应生成推荐的机制。特别是，我们利用概率矩阵分解算法和最近邻算法系列的预测分数的不确定性、对最近邻算法的预测分数的支持以及独立于算法的方法。我们研究了在某些情况下决定不推荐时推荐算法的性能如何演变。如果避免推荐的决定是明智的——即，不是随机的，而是与系统可用的关于目标用户或项目的信息相关——性能的提高会以牺牲其他质量维度为代价，例如覆盖率、新颖性或多样性。这种平衡是至关重要的，因为可以实现非常高的精度，只向唯一用户推荐一个项目，这不会是一个非常有用的推荐器。因此，一方面，我们探索了一些技术来结合精度和覆盖率指标，这是该领域的一个悬而未决的问题。另一方面，这里提出了一系列基于避免推荐而不是提供不良推荐的假设的度量（正确性）。总之，本文的贡献有两个方面：可应用于某些推荐系统系列的技术分类法，允许包括决定是否应生成推荐的机制，以及对评估指标组合的首次探索，主要侧重于精度和覆盖率的测量。经验结果表明，当使用这些方法以牺牲用户和项目覆盖率以及不同程度的新颖性和多样性为代价时，可以获得很大的精度改进。