  Dating and romantic relationships not only play a huge role in our personal
lives but also collectively influence and shape society. Today, many romantic
partnerships originate from the Internet, signifying the importance of
technology and the web in modern dating. In this paper, we present a text-based
computational approach for estimating the relationship compatibility of two
users on social media. Unlike many previous works that propose reciprocal
recommender systems for online dating websites, we devise a distant supervision
heuristic to obtain real world couples from social platforms such as Twitter.
Our approach, the CoupleNet is an end-to-end deep learning based estimator that
analyzes the social profiles of two users and subsequently performs a
similarity match between the users. Intuitively, our approach performs both
user profiling and match-making within a unified end-to-end framework.
CoupleNet utilizes hierarchical recurrent neural models for learning
representations of user profiles and subsequently coupled attention mechanisms
to fuse information aggregated from two users. To the best of our knowledge,
our approach is the first data-driven deep learning approach for our novel
relationship recommendation problem. We benchmark our CoupleNet against several
machine learning and deep learning baselines. Experimental results show that
our approach outperforms all approaches significantly in terms of precision.
Qualitative analysis shows that our model is capable of also producing
explainable results to users.
