Performance of Hyperbolic Geometry Models on Top-N Recommendation Tasks-Reference-Cited by-同舟云学术

Performance of Hyperbolic Geometry Models on Top-N Recommendation Tasks

Published:2020-09-22 Issue: Volume: Page:
ISSN:
Container-title:Fourteenth ACM Conference on Recommender Systems
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Author:

Mirvakhabova Leyla¹,Frolov Evgeny¹,Khrulkov Valentin¹,Oseledets Ivan¹,Tuzhilin Alexander²

Affiliation:

1. Skolkovo Institute of Science and Technology, Russia

2. New York University, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3383313.3412219

Reference27 articles.

1. Justin Basilico. 2019. Recent Trends in Personalization: A Netflix Perspective. Justin Basilico. 2019. Recent Trends in Personalization: A Netflix Perspective.

2. Stochastic Gradient Descent on Riemannian Manifolds

3. Pedro Cano Oscar Celma Markus Koppenberger and Javier M Buldu. 2006. Topology of music recommendation networks. Chaos: An interdisciplinary journal of nonlinear science 16 1(2006) 013107. Pedro Cano Oscar Celma Markus Koppenberger and Javier M Buldu. 2006. Topology of music recommendation networks. Chaos: An interdisciplinary journal of nonlinear science 16 1(2006) 013107.

4. Benjamin Paul Chamberlain Stephen R Hardwick David R Wardrope Fabon Dzogang Fabio Daolio and Saúl Vargas. 2019. Scalable Hyperbolic Recommender Systems. arXiv preprint arXiv:1902.08648(2019). Benjamin Paul Chamberlain Stephen R Hardwick David R Wardrope Fabon Dzogang Fabio Daolio and Saúl Vargas. 2019. Scalable Hyperbolic Recommender Systems. arXiv preprint arXiv:1902.08648(2019).

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