A generalized taxonomy of explanations styles for traditional and social recommender systems
A generalized taxonomy of explanations styles for traditional and social recommender systems
复制标题
DOI：
10.1007/s10618-011-0215-0
复制
发表时间：
2012-05
影响因子：
4.8
通讯作者：
Alexis Papadimitriou;P. Symeonidis;Y. Manolopoulos
中科院分区：
文献类型：
--
作者：
Alexis Papadimitriou;P. Symeonidis;Y. Manolopoulos
推荐系统通常会提供其推荐的解释，以更好地帮助用户选择产品、活动甚至朋友。到目前为止，解释风格的类型是根据使用它的推荐系统来考虑的。这种关系是一对一的，这意味着对于每个不同的推荐系统类别，有一个不同的解释风格类别。然而，这种一对一的对应关系可能被认为过于简单和不可推广。相反，我们考虑可以用于解释的三种基本资源：用户、项目和功能以及它们的任何组合。在本次调查中，我们定义了(i) Human style of explanation，它基于相似的用户提供解释；（ii） Item style of explanation，它基于用户对相似项目的选择；（iii） Feature style of explanation，它基于用户事先评价的项目特征来解释推荐。通过使用上述风格的任意组合，我们也可以定义混合风格的解释。我们通过介绍采用这些风格的推荐系统来演示如何将这些风格付诸实践。此外，由于社交网络对当代推荐系统的影响及其解释风格的研究不足，我们研究了新出现的社交推荐系统，即Facebook Connect解释（HuffPo， Netflix等）和将地理与社交数据结合起来的地理社会解释（Gowalla， Facebook Places等）。最后，我们总结了三种不同用户研究的结果，以支持混合是最有效的解释风格，因为它包含了所有其他风格。
Recommender systems usually provide explanations of their recommendations to better help users to choose products, activities or even friends. Up until now, the type of an explanation style was considered in accordance to the recommender system that employed it. This relation was one-to-one, meaning that for each different recommender systems category, there was a different explanation style category. However, this kind of one-to-one correspondence can be considered as over-simplistic and non generalizable. In contrast, we consider three fundamental resources that can be used in an explanation: users, items and features and any combination of them. In this survey, we define (i) the Human style of explanation, which provides explanations based on similar users, (ii) the Item style of explanation, which is based on choices made by a user on similar items and (iii) the Feature style of explanation, which explains the recommendation based on item features rated by the user beforehand. By using any combination of the aforementioned styles we can also define the Hybrid style of explanation. We demonstrate how these styles are put into practice, by presenting recommender systems that employ them. Moreover, since there is inadequate research in the impact of social web in contemporary recommender systems and their explanation styles, we study new emerged social recommender systems i.e. Facebook Connect explanations (HuffPo, Netflix, etc.) and geo-social explanations that combine geographical with social data (Gowalla, Facebook Places, etc.). Finally, we summarize the results of three different user studies, to support that Hybrid is the most effective explanation style, since it incorporates all other styles.