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Subprofile-aware diversification of recommendations

机译：子配置文件感知的建议多样化

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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：如果每个单独的推荐与其相关，则推荐系统的用户更可能对一个或多个推荐感到满意，但是如果推荐的集合是多种多样的，则该推荐系统的用户更可能对一个或多个推荐感到满意。推荐多样化的最常见方法是使用重新排名：推荐系统对与用户相关的一组候选项目进行评分；然后，它会对候选者重新排序，以便它推荐的子集在相关性和多样性之间达到平衡。通常，我们期望在相关性和多样性之间进行权衡：建议集合的多样性通过包含相关性得分较低但与集合中已有项目不同的项目而增加。在早期工作中，根据在项目特征（例如电影体裁）上定义的一些语义距离度量，一组建议的多样性是由它们彼此之间的平均距离给出的。较新的意图感知多样化方法根据方面的覆盖范围和相关性来制定多样性。方面通常是根据项目功能定义的。通过尝试确保推荐项目集的各个方面涵盖了用户个人资料中项目的各个方面，多样性程度更加个性化。在预先收集的数据集的脱机实验中，使用项目特征作为方面的意图感知多样化有时无视相关性/多样性的权衡关系：在某些配置中，建议在相关性和多样性上均表现出增加。在本文中，我们提出了一种新的意图感知多元化形式，我们将其称为SPAD（Subprofile-Aware Diversification）和一种名为RSPAD（基于相关性的SPAD）的变体。在SPAD中，方面不是项目功能；它们是用户个人资料的子个人资料。我们介绍并比较了许多不同的方法来从用户的个人资料中提取子个人资料。没有根据项目功能定义它们。因此，SPAD甚至在项目功能不可用或质量较低的领域中也很有用。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与意图方面的方法进行了比较，其中方面是项特征。在这些数据集上，我们发现SPAD和RSPAD受相关性/多样性折衷的影响甚至更少：在所有三个数据集中，与其他多元化方法相比，它们在增加配置的同时提高了相关性和多样性。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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