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Dating and romantic relationships not only play a huge role in our personallives but also collectively influence and shape society. Today, many romanticpartnerships originate from the Internet, signifying the importance oftechnology and the web in modern dating. In this paper, we present a text-basedcomputational approach for estimating the relationship compatibility of twousers on social media. Unlike many previous works that propose reciprocalrecommender systems for online dating websites, we devise a distant supervisionheuristic to obtain real world couples from social platforms such as Twitter.Our approach, the CoupleNet is an end-to-end deep learning based estimator thatanalyzes the social profiles of two users and subsequently performs asimilarity match between the users. Intuitively, our approach performs bothuser profiling and match-making within a unified end-to-end framework.CoupleNet utilizes hierarchical recurrent neural models for learningrepresentations of user profiles and subsequently coupled attention mechanismsto fuse information aggregated from two users. To the best of our knowledge,our approach is the first data-driven deep learning approach for our novelrelationship recommendation problem. We benchmark our CoupleNet against severalmachine learning and deep learning baselines. Experimental results show thatour approach outperforms all approaches significantly in terms of precision.Qualitative analysis shows that our model is capable of also producingexplainable results to users.

CoupleNet: Paying Attention to Couples with Coupled Attention for Relationship Recommendation