A survey of active learning in collaborative filtering recommender systems
A survey of active learning in collaborative filtering recommender systems
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DOI：
10.1016/j.cosrev.2016.05.002
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发表时间：
2016-05-01
影响因子：
12.9
通讯作者：
Rubens, Neil
中科院分区：
文献类型：
--
作者：
Elahi, Mehdi;Ricci, Francesco;Rubens, Neil
在协同过滤推荐系统中，用户的偏好被表示为对项目的评分，并且每个额外的评分扩展了系统的知识并影响系统的推荐准确性。一般来说，从用户那里得到的评级越多，推荐就越有效。然而，每个评级的有用性可能差异很大，即，不同的评级可以带来关于用户的品味的不同数量和类型的信息。因此，被定义为“主动学习策略”的特定技术可以用于选择性地选择要呈现给用户以进行评级的项目。事实上，主动学习策略识别并采用标准来获取更好地反映用户偏好的数据，并能够生成更好的解释。到目前为止，文献中已经提出了各种主动学习策略。在这篇文章中，我们调查了最近的战略，将它们分为两个不同的维度：个性化，即，系统选择的项目对于不同的用户是否不同，以及混合，即，主动学习是由单个标准（启发式）还是由多个标准引导。此外，我们提出了一个全面的概述的评价方法和指标，已采用的研究社区，以测试主动学习策略的协同过滤。最后，我们比较了调查的策略，并提供了他们的使用在推荐系统的指导方针。（C）. 2016 Elsevier Inc. All rights reserved.
In collaborative filtering recommender systems user's preferences are expressed as ratings for items, and each additional rating extends the knowledge of the system and affects the system's recommendation accuracy. In general, the more ratings are elicited from the users, the more effective the recommendations are. However, the usefulness of each rating may vary significantly, i.e., different ratings may bring a different amount and type of information about the user's tastes. Hence, specific techniques, which are defined as "active learning strategies", can be used to selectively choose the items to be presented to the user for rating. In fact, an active learning strategy identifies and adopts criteria for obtaining data that better reflects users' preferences and enables to generate better recommendations.So far, a variety of active learning strategies have been proposed in the literature. In this article, we survey recent strategies by grouping them with respect to two distinct dimensions: personalization, i.e., whether the system selected items are different for different users or not, and, hybridization, i.e., whether active learning is guided by a single criterion (heuristic) or by multiple criteria. In addition, we present a comprehensive overview of the evaluation methods and metrics that have been employed by the research community in order to test active learning strategies for collaborative filtering. Finally, we compare the surveyed strategies and provide guidelines for their usage in recommender systems. (C). 2016 Elsevier Inc. All rights reserved.