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

By Yi Tay, Anh Tuan Luu, Siu Cheung Hui. 2018

27 citations recorded by the source corpus.

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 basedestimator 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 benchmarkour CoupleNet against several machine learning and deep learning baselines. Experimental results show that our approach outperformsall approaches significantly in terms of precision. Qualitative analysis shows that our model is capable of also producing explainable results to users.

Subfields: Artificial Intelligence, Information Systems, Social Psychology, Recommender Systems

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Citations in the corpus

Analysis Methods in Neural Language Processing: A Survey (2018, methodology): “…have produced visualizations ranging from tasks like NLI (Rocktäschel et al., 2016; Yin et al., 2016), summarization (Rush et al., 2015), MT post-editing (Jauregi Unanue et al., 2018), and morphological inﬂection (Aharoni and Goldberg, 2017), to matching users on social media (Tay et al., 2018).”