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Michinobu Shimatani

dblp:294/1750 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Recommender systems · 100%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Recommender systems › user recommendation
reciprocal recommendation		 0.512021
A Reciprocal Embedding Framework For Modelling Mutual Preferences · AAAI 2021

Methods — techniques the papers use, named apart from their topics

matrix factorization · 0.5embedding · 0.5
YearPublicationVenuePosition
2021 A Reciprocal Embedding Framework For Modelling Mutual Preferences
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.
R. Ramanathan 0002, Nicolas K. Shinada, Michinobu Shimatani, Yuhei Yamaguchi, Junichi Tanaka, Yuta Iizuka, Sucheendra K. Palaniappan
 AAAI3