Many social services including online dating, social media, recruitment and online learning, largely rely on%DIFDELCMD < \matching %%% people with the right people". The success of these services and the user experience with them often depends on their ability to match users.Reciprocal Recommender Systems (RRS) arose to facilitate this process by identifying users who are a potential match for each other, based on information provided by them. These systems are inherently more complex than user-item recommendation approaches and unidirectional user recommendationservices, since they need to take into account both users ' preferences towards each other in the recommendation process. This entails not only predicting accurate preference estimates  as classical recommenders do, but also defining adequate fusion processes for aggregating user-to-user preferential information. The latter is a crucial and distinctive, yet barely investigated aspect in RRS research. This paper presents a snapshot analysis of the extant literature to summarize the state-of-the-art RRS research to date, focusing on the fundamental features that differentiate RRSs from other classes of recommender systems . 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.
%DIFDELCMD < \matching %%% 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.
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Many social services including online dating, social media, recruitment and online learning, largely rely on
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people with the right people". The success of these services and the user experience with them often depends on their ability to match users.Reciprocal Recommender Systems (RRS) arose to facilitate this process by identifying users who are a potential match for each other, based on information provided by them. These systems are inherently more complex than user-item recommendation approaches and unidirectional user recommendationservices, since they need to take into account both users ' preferences towards each other in the recommendation process. This
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
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upon user interaction data
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defining adequate fusion processes for aggregating
calculating mutual compatibility between (pairs of) users, typically by applying fusion processes on unilateral
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preferential information. The latter is a crucial and distinctive, yet barely investigated aspect in RRS research.
preference information.
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snapshot
snapshot-style
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to summarize
that summarizes
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fundamental features that differentiate RRSs from other classes of recommender systems
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