Latent factor models and aggregation operators for collaborative filtering in reciprocal recommender systems

Abstract

Online dating platforms help to connect people who might potentially be a good match for each other. They have exerted a significant societal impact over the last decade, such that about one third of new relationships in the US are now started online, for instance. Recommender Systems are widely utilized in online platforms that connect people to people in e.g. online dating and recruitment sites. These recommender approaches are fundamentally different from traditional user-item approaches (such as those operating on movie and shopping sites), in that they must consider the interests of both parties jointly. Latent factor models have been notably successful in the area of user-item recommendation, however they have not been investigated within user-to-user domains as of yet. In this study, we present a novel method for reciprocal recommendation using latent factor models. We also provide a first analysis of the use of different preference aggregation strategies, thereby demonstrating that the aggregation function used to combine user preference scores has a significant impact on the outcome of the recommender system. Our evaluation results report significant improvements over previous nearest-neighbour and content-based methods for reciprocal recommendation, and show that the latent factor model can be used effectively on much larger datasets than previous state-of-the-art reciprocal recommender systems.