There exist situations of decision-making under information overload in the Internet, where people have an overwhelming number of available options to choose from, e.g. products to buy in an e-commerce site, or restaurants to visit in a large city. Recommender systems arose as a data-driven personalized decision support tool to assist users in these situations: they are able to process user-related data, filtering and recommending items based on the user’s preferences, needs and/or behavior. Unlike most conventional recommender approaches where items are inanimate entities recommended to the users and success is solely determined upon the end user’s reaction to the recommendation(s) received, in a Reciprocal Recommender System (RRS) users become the item being recommended to other users. Hence, both the end user and the user being recommended should accept the “matching” recommendation to yield a successful RRS performance. The operation of an RRS entails not only predicting accurate preference estimates upon user interaction data as classical recommenders do, but also calculating mutual compatibility between (pairs of) users, typically by applying fusion processes on unilateral user-to-user preference information. This paper presents a snapshot-style analysis of the extant literature that summarizes the state-of-the-art RRS research to date, focusing on the algorithms, fusion processes and fundamental characteristics of RRS, both inherited from conventional user-to-item recommendation models and those inherent to this emerging family of approaches. Representative RRS models are likewise highlighted. Following this, we discuss the challenges and opportunities for future research on RRSs, with special focus on (i) fusion strategies to account for reciprocity and (ii) emerging application domains related to social recommendation.

Internet上存在信息过载的情况下决策的情况，人们有大量可供选择的选择，例如，在电子商务站点上购买的产品或在大城市中参观的餐馆。推荐系统应运而生，它是一种数据驱动的个性化决策支持工具，可在以下情况下为用户提供帮助：他们能够处理与用户有关的数据，根据用户的喜好，需求和/或行为过滤和推荐商品。与大多数传统的推荐器方法不同，项目是向用户推荐的无生命实体，并且成功仅取决于最终用户对收到的推荐的反应来确定，在对等推荐系统（RRS）中，用户成为向其他用户推荐的项目。因此，最终用户和被推荐的用户都应接受“匹配”推荐以产生成功的RRS性能。RRS的操作不仅需要像经典推荐者一样根据用户交互数据预测准确的偏好估计，而且还需要计算（成对的）用户之间的相互兼容性，通常是通过对单方面的用户到用户的偏好信息应用融合过程。本文对现有文献进行了快照式分析，总结了迄今为止最先进的RRS研究，重点是RRS的算法，融合过程和基本特征，均继承自常规的用户对项目推荐模型以及这种新兴方法系列所固有的模型。代表性的RRS模型同样被强调。按照此，