Deep Feature Interaction Embedding for Pair Matching Prediction

Abstract

Online dating services have become popular in modern society. Pair matching prediction between two users in these services can help efficiently increase the possibility of finding their life partners. Deep learning based methods with automatic feature interaction functions such as Factorization Machines (FM) and cross network of Deep & Cross Network (DCN) can model sparse categorical features, which are effective to many recommendation tasks of web applications. To solve the partner recommendation task, we improve these FM-based deep models and DCN by enhancing the representation of feature interaction embedding and proposing a novel design of interaction layer avoiding information loss. Through the experiments on two real-world datasets of two online dating companies, we demonstrate the superior performances of our proposed designs.