Fetching the paper…
Reading the bibliography…
Item cold-start is a classical issue in recommender systems that affects anime and manga recommendations as well.
“Regression shrinkage and selection via the lasso”
Robert Tibshirani · 1996
Earlier work this paper cites.
“Hybrid recommender systems: Survey and experiments”
Robin Burke · 2002
Earlier work this paper cites.
“Large-scale parallel collaborative filtering for the netflix prize”
Yunhong Zhou, Dennis Wilkinson, Robert Schreiber and Rong Pan · 2008
Earlier work this paper cites.
“The bellkor solution to the netflix grand prize”
Yehuda Koren · 2009
Earlier work this paper cites.
“Feature-weighted linear stacking”
Joseph Sill, G“’abor Tak“’acs, Lester Mackey and David Lin · 2009
Earlier work this paper cites.
“Combining predictions for accurate recommender systems”
Michael Jahrer, Andreas T“”oscher and Robert Legenstein · 2010
Earlier work this paper cites.
“Factorization machines”
Steffen Rendle · 2010
Earlier work this paper cites.
“Matrix co-factorization for recommendation with rich side information and implicit feedback”
Yi Fang and Luo Si · 2011
Earlier work this paper cites.
“Optimal recommender systems blending”
Fabio Roda, Alberto Costa and Leo Liberti · 2011
Earlier work this paper cites.
“A collaborative filtering approach to mitigate the new user cold start problem”
Jes“’uS Bobadilla, Fernando Ortega, Antonio Hernando and Jes“’uS Bernal · 2012
Earlier work this paper cites.
“Socially enabled preference learning from implicit feedback data”
Julien Delporte, Alexandros Karatzoglou, Tomasz Matuszczyk and St“’ephane Canu · 2013
Cited alongside, same era.
“Deep content-based music recommendation”
Aaron Van, Sander Dieleman and Benjamin Schrauwen · 2013
Cited alongside, same era.
“Speedup matrix completion with side information: Application to multi-label learning”
Miao Xu, Rong Jin and Zhi-Hua Zhou · 2013
Cited alongside, same era.
“Scalable variational bayesian matrix factorization with side information”
Yong-Deok Kim and Seungjin Choi · 2014
Cited alongside, same era.
“Very deep convolutional networks for large-scale image recognition”
Karen Simonyan and Andrew Zisserman · 2014
Cited alongside, same era.
“The MovieLens Datasets: History and Context”
“Deep neural networks for youtube recommendations”
Paul Covington, Jay Adams and Emre Sargin · 2016
Later among the works it cites.
“Deep residual learning for image recognition”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2016
Later among the works it cites.
“Learning Hidden Features for Contextual Bandits”
Huazheng Wang, Qingyun Wu and Hongning Wang · 2016
Later among the works it cites.
“Localized Lasso for High-Dimensional Regression”
Makoto Yamada, Koh Takeuchi, Tomoharu Iwata, John Shawe-Taylor and Samuel Kaski · 2016
Later among the works it cites.
“Matrix factorization+ for movie recommendation”
Lili Zhao, Zhongqi Lu, Sinno Plan and Qiang Yang · 2016
Later among the works it cites.
“ParVecMF: A Paragraph Vector-based Matrix Factorization Recommender System”
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
F. Harper and Joseph. Konstan · 2015
Cited alongside, same era.
“Metadata embeddings for user and item cold-start recommendations”
Maciej Kula · 2015
Cited alongside, same era.
“Illustration2Vec: A Semantic Vector Representation of Illustrations”
Masaki Saito and Yusuke Matsui · 2015
Cited alongside, same era.
“Mangaki: an Anime/Manga Recommender System with Fast Preference Elicitation”
Jill-J“ˆenn Vie, Camille La“”ly and Sol“‘ene Pichereau · 2015
Cited alongside, same era.
“Recommender systems”
Charu Aggarwal · 2016
Cited alongside, same era.
Georgios Alexandridis, Georgios Siolas and Andreas Stafylopatis · 2017
Closest in time.
“Combating the Cold Start User Problem in Model Based Collaborative Filtering”
Sampoorna Biswas, Laks Lakshmanan and Senjuti Ray · 2017
Closest in time.
“Specializing Joint Representations for the task of Product Recommendation”
Thomas Nedelec, Elena Smirnova and Flavian Vasile · 2017
Closest in time.
“On the consistency of ordinal regression methods”
Fabian Pedregosa, Francis Bach and Alexandre Gramfort · 2017
Closest in time.
“Collaborative filtering and deep learning based recommendation system for cold start items”
Jian Wei, Jianhua He, Kai Chen, Yi Zhou and Zuoyin Tang · 2017
Closest in time.