What You Like, What I Am: Online Dating Recommendation via Matching Individual Preferences With Features

Dating recommendation becomes a critical task since the rapidly development of the online dating sites and it is beneficial for users to find their ideal relationships from a large number of registered members. Different users usually have different tastes when choosing their dating partners. Therefore, it is necessary to distinguish the user's personal features and preferences in dating recommendation methods. However, present approaches don't capture enough user preferences from social graph and attribute data. They also ignore user attributes, which is the complementary and consistent side information of user social graphs. In this paper, we propose a Matching Individual Preferences with Features (MIPF) model to recommend dating partners jointly using user attributes and social graphs. Our specific design in the model is that user preferences and user features are completely different. User features are users' personal characteristics, while user preferences indicate which kind of other users they are seeking. We aim to model user features and preferences to identify what the user is and what the user likes. We also distinguish user preferences into explicit preference and implicit preferences. The implicit preferences are mined from social graphs, while the explicit preferences are captured from the social links. Additionally, convolutional neural networks are used to extract the latent non-linear information in user attributes. Experiments on real-world online dating datasets demonstrate our MIPF model is superior to existing methods.