Reciprocal recommender system for online dating
Moreover, they may also be the subject of intense interest from existing users now that their profile is public (often a site will heavily promote newly joined users).
The Decision Support and Recommender Systems (DSRS) research group was founded in mid-2018 by researchers in the School of Computer Science, Electrical and Electronic Engineering, and Engineering Maths (SCEEM), University of Bristol.
Thus a people-to-people recommender system needs to take into account the preferences of proposed candidates, who determine whether an interaction is successful, in addition to those of the user.
People-to-people recommenders are thus reciprocal recommenders (Pizzato et al. Or, putting this from the point of view of the user, a people-to-people recommender system must take into account both a user's taste (whom they find attractive) and their own attractiveness, so the presented candidates will find them attractive in return, and give a positive reply (Cai et al. Using online dating sites can be difficult for many people.
The results show that our recommendation algorithms significantly outperform previously proposed approaches, and the collaborative filtering-based algorithms achieve much better performance than content-based algorithms in both precision and recall.Fourth, as is well known, dating is a type of market, a matching market, where people compete for scarce resources (partners) and are, in turn, a resource sought by others--see the recent user perspective of a labor market economist on the dating market that explores this idea in depth (Oyer 2014).