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 users
preferences, needs and/or behaviour. Unlike most conventional recommender
approaches where items are inanimate entities recommended to the users and
success is solely determined upon the end users 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.

本文介绍了先进推荐系统技术的快照，重点关注了 Recommender Systems，Reciprocal Recommender System 等 RRS 算法，以及融合过程和社交推荐应用的挑战和机遇。