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Subprofile-aware diversification of recommendations

机译：子项目意识推荐的多样化

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摘要

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.
机译：如果每个人的建议与她相关，则推荐系统的用户更有可能满足其中一个或多个建议，但如果这一组建议是多样化的。推荐多样化最常见的方法使用重新排名：推荐系统得分一组候选项目，以便与用户相关;然后，它重新排名候选人，以便它建议推荐的子集实现相关性和多样性之间的平衡。通常，我们预计相关性和多样性之间的权衡：这一组建议的多样性通过包括具有较低相关性分数但与已经在集合中的物品不同的物品来增加。在早期工作中，根据在电影类型等项目特征上定义的一些语义距离度量，通过彼此的距离的平均值给出了一系列建议的多样性。最近的意图传染性方法在覆盖范围和方面的相关方面制定多样性。这些方面最常在项目特征方面定义。通过尝试确保一组推荐项目的方面涵盖了用户的配置文件中项目的各个方面，多样性级别更为个性化。在离线实验上进行预收集的数据集，使用项目特征的意思感知多样化有时会忽略相关性/多样性权衡：有配置在其中，建议表现出相关性和多样性增加。在本文中，我们介绍了一种新的意图感知多样化，我们调用SPAD（子专业感知多样化）和称为RSPAD（基于相关的SPAD）的变体。在SPAD中，方面不是项目特征;它们是用户个人资料的子项源。我们展示并比较了许多不同的方法来从用户的配置文件中提取子项目。它们都不是项目特征的定义。因此，即使在项目特征不可用或质量低的域中，SPAD也很有用。在三个不同域名的三个预收集的数据集（电影，音乐艺术家和书籍）上，我们将SPAD和RSPAD与INTEN感知的方法进行比较，其中各个方面是项目功能。我们在这些数据集中发现，SPAD和RSPAD的相关性/多样性折衷甚至遭受较少的折衷：在所有三个数据集中，它们可以增加比其他多样化方法的配置更有配置。此外，我们发现SPAD和RSPAD是所有三个数据集中最准确的系统。

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