Photos Are All You Need for Reciprocal Recommendation in Online Dating
This addresses the challenge of improving match quality for users in online dating services by leveraging underutilized image data, representing an incremental advance in reciprocal recommendation systems.
The paper tackled the problem of reciprocal recommendation in online dating by using only user photographs to predict bidirectional preferences, achieving an F1 score of 0.87 on a real-world dataset and outperforming state-of-the-art methods.
Recommender Systems are algorithms that predict a user's preference for an item. Reciprocal Recommenders are a subset of recommender systems, where the items in question are people, and the objective is therefore to predict a bidirectional preference relation. They are used in settings such as online dating services and social networks. In particular, images provided by users are a crucial part of user preference, and one that is not exploited much in the literature. We present a novel method of interpreting user image preference history and using this to make recommendations. We train a recurrent neural network to learn a user's preferences and make predictions of reciprocal preference relations that can be used to make recommendations that satisfy both users. We show that our proposed system achieves an F1 score of 0.87 when using only photographs to produce reciprocal recommendations on a large real world online dating dataset. Our system significantly outperforms on the state of the art in both content-based and collaborative filtering systems.