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There exist situations of decision-making under information overload in theInternet, where people have an overwhelming number of available options tochoose from, e.g. products to buy in an e-commerce site, or restaurants tovisit in a large city. Recommender systems arose as a data-driven personalizeddecision support tool to assist users in these situations: they are able toprocess user-related data, filtering and recommending items based on the userspreferences, needs and/or behaviour. Unlike most conventional recommenderapproaches where items are inanimate entities recommended to the users andsuccess is solely determined upon the end users reaction to therecommendation(s) received, in a Reciprocal Recommender System (RRS) usersbecome the item being recommended to other users. Hence, both the end user andthe user being recommended should accept the 'matching' recommendation to yielda successful RRS performance. The operation of an RRS entails not onlypredicting accurate preference estimates upon user interaction data asclassical recommenders do, but also calculating mutual compatibility between(pairs of) users, typically by applying fusion processes on unilateraluser-to-user preference information. This paper presents a snapshot-styleanalysis of the extant literature that summarizes the state-of-the-art RRSresearch to date, focusing on the algorithms, fusion processes and fundamentalcharacteristics of RRS, both inherited from conventional user-to-itemrecommendation models and those inherent to this emerging family of approaches.Representative RRS models are likewise highlighted. Following this, we discussthe challenges and opportunities for future research on RRSs, with specialfocus on (i) fusion strategies to account for reciprocity and (ii) emergingapplication domains related to social recommendation.

Reciprocal Recommender Systems: Analysis of State-of-Art Literature, Challenges and Opportunities towards Social Recommendation