A Reciprocal Embedding Framework For Modelling Mutual Preferences
Keywords:Recommender Systems, Learning Latent Representations, Collaborative Filtering, Social Recommendation, Personalization
AbstractUnderstanding the mutual preferences between potential dating partners is core to the success of modern web-scale personalized recommendation systems that power online dating platforms. In contrast to classical user-item recommendation systems which model the unidirectional preferences of users to items, understanding the bidirectional preferences between people in a reciprocal recommendation system is more complex and challenging given the dynamic nature of interactions. In this paper, we describe a reciprocal recommendation system we built for one of the leading online dating applications in Japan. We also discuss the lessons learnt from designing, developing and deploying the reciprocal recommendation system in production.
How to Cite
Ramanathan, R., Shinada, N. K., Shimatani, M., Yamaguchi, Y., Tanaka, J., Iizuka, Y., & Palaniappan, S. K. (2021). A Reciprocal Embedding Framework For Modelling Mutual Preferences. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15385-15392. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17807
IAAI Technical Track on Emerging Applications of AI