A user of a recommender system is more likely to be satisfied by one or more of the recommendations if each individual recommendation is relevant to her but additionally if the set of recommendations is diverse. The most common approach to recommendation diversification uses re-ranking: the recommender system scores a set of candidate items for relevance to the user; it then re-ranks the candidates so that the subset that it will recommend achieves a balance between relevance and diversity. Ordinarily, we expect a trade-off between relevance and diversity: the diversity of the set of recommendations increases by including items that have lower relevance scores but which are different from the items already in the set. In early work, the diversity of a set of recommendations was given by the average of their distances from one another, according to some semantic distance metric defined on item features such as movie genres. More recent intent-aware diversification methods formulate diversity in terms of coverage and relevance of aspects. The aspects are most commonly defined in terms of item features. By trying to ensure that the aspects of a set of recommended items cover the aspects of the items in the user’s profile, the level of diversity is more personalized. In offline experiments on pre-collected datasets, intent-aware diversification using item features as aspects sometimes defies the relevance/diversity trade-off: there are configurations in which the recommendations exhibits increases in both relevance and diversity. In this paper, we present a new form of intent-aware diversification, which we call SPAD (Subprofile-Aware Diversification), and a variant called RSPAD (Relevance-based SPAD). In SPAD, the aspects are not item features; they are subprofiles of the user’s profile. We present and compare a number of different ways to extract subprofiles from a user’s profile. None of them is defined in terms of item features. Therefore, SPAD is useful even in domains where item features are not available or are of low quality. On three pre-collected datasets from three different domains (movies, music artists and books), we compare SPAD and RSPAD to intent-aware methods in which aspects are item features. We find on these datasets that SPAD and RSPAD suffer even less from the relevance/diversity trade-off: across all three datasets, they increase both relevance and diversity for even more configurations than other approaches to diversification. Moreover, we find that SPAD and RSPAD are the most accurate systems across all three datasets.

中文翻译：

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建议的子配置感知多样化

如果每个单独的推荐都与推荐系统的用户相关，那么推荐系统的用户更有可能对一个或多个推荐感到满意，但另外，如果推荐集是多样化的。最常见的推荐多样化方法使用重新排序：推荐系统根据与用户的相关性对一组候选项目进行评分；然后它对候选者重新排序，以便它推荐的子集在相关性和多样性之间取得平衡。通常，我们期望在相关性和多样性之间进行权衡：通过包含具有较低相关性分数但与集合中已有的项目不同的项目，推荐集的多样性会增加。在早期的工作中，一组推荐的多样性是由它们彼此之间的距离的平均值给出的，根据在项目特征（如电影类型）上定义的一些语义距离度量。最近的意图感知多样化方法在方面的覆盖范围和相关性方面制定了多样性。这些方面最常根据项目特征来定义。通过尝试确保一组推荐项目的方面覆盖用户个人资料中项目的方面，多样性水平更加个性化。在预先收集的数据集的离线实验中，使用项目特征作为方面的意图感知多样化有时会违背相关性/多样性的权衡：在某些配置中，推荐在相关性和多样性方面都表现出增加。在本文中，我们提出了一种新形式的意图感知多样化，我们将其称为 SPAD（Subprofile-Aware Diversification），和一个称为 RSPAD（基于相关性的 SPAD）的变体。在SPAD中，方面不是项目特征；它们是用户配置文件的子配置文件。我们展示并比较了从用户个人资料中提取子个人资料的多种不同方法。它们都不是根据项目特征来定义的。因此，即使在项目特征不可用或质量低的领域中，SPAD 也很有用。在来自三个不同领域（电影、音乐艺术家和书籍）的三个预先收集的数据集上，我们将 SPAD 和 RSPAD 与其中方面是项目特征的意图感知方法进行了比较。我们发现在这些数据集上，SPAD 和 RSPAD 受到相关性/多样性权衡的影响更少：在所有三个数据集中，与其他多样化方法相比，它们增加了更多配置的相关性和多样性。而且，