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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 > No. 17: IAAI-21, EAAI-21, AAAI-21 Special Programs and Special Track

A Reciprocal Embedding Framework For Modelling Mutual Preferences

Authors

R. Ramanathan

SBX Technologies Corporation, Tokyo


Nicolas K. Shinada

SBX Technologies Corporation, Tokyo


Michinobu Shimatani

Tapple Inc., Tokyo


Yuhei Yamaguchi

Tapple Inc., Tokyo


Junichi Tanaka

Tapple Inc., Tokyo


Yuta Iizuka

Tapple Inc., Tokyo


Sucheendra K. Palaniappan

SBX Technologies Corporation, Tokyo


Proceedings:

No. 17: IAAI-21, EAAI-21, AAAI-21 Special Programs and Special Track

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 35

Track:

IAAI Technical Track on Emerging Applications of AI

Downloads:

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Abstract:

Understanding 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.

DOI:

10.1609/aaai.v35i17.17807


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 35