Diversification of recommendation results is a promising approach for coping with the uncertainty associated with users' information needs. Of particular importance in diversified recommendation is to define and optimize an appropriate diversity objective. In this study, we revisit the most popular diversity objective called intra-list distance (ILD), defined as the average pairwise distance between selected items, and a similar but lesser known objective called dispersion, which is the minimum pairwise distance. Owing to their simplicity and flexibility, ILD and dispersion have been used in a plethora of diversified recommendation research. Nevertheless, we do not actually know what kind of items are preferred by them. We present a critical reexamination of ILD and dispersion from theoretical and experimental perspectives. Our theoretical results reveal that these objectives have potential drawbacks: ILD may select duplicate items that are very close to each other, whereas dispersion may overlook distant item pairs. As a competitor to ILD and dispersion, we design a diversity objective called Gaussian ILD, which can interpolate between ILD and dispersion by tuning the bandwidth parameter. We verify our theoretical results by experimental results using real-world data and confirm the extreme behavior of ILD and dispersion in practice.

翻译：推荐结果多样化是应对用户信息需求不确定性的一种有前景的方法。在多样化推荐中，定义并优化恰当的多样性目标尤为重要。本研究重新审视了最流行的多样性目标——列表内距离（intra-list distance, ILD），定义为选定项之间成对距离的平均值；以及一个类似但较少为人知的目标——离散度（dispersion），即最小成对距离。由于简便性和灵活性，ILD和离散度已被广泛应用于多样化推荐研究中。然而，我们实际上并不清楚它们偏好何种类型的项目。本文从理论和实验角度对ILD和离散度进行了批判性再审视。理论结果表明，这些目标存在潜在缺陷：ILD可能选择彼此非常接近的重复项，而离散度可能忽略相距较远的项对。作为ILD和离散度的替代方案，我们设计了一种名为高斯ILD（Gaussian ILD）的多样性目标，通过调节带宽参数可在ILD和离散度之间插值。我们使用真实世界数据的实验结果验证了理论分析，并确认了ILD和离散度在实际中的极端行为。