Matrix factorization (Italian Wikipedia)

Analysis of information sources in references of the Wikipedia article "Matrix factorization" in Italian language version.

refsWebsite

Global rank Italian rank

14doi.org

2^nd place

7^th place

11oadoi.org

799^th place

22^nd place

3acm.org

1,185^th place

1,964^th place

1sifter.org

low place

1mimuw.edu.pl

low place

1arxiv.org

69^th place

235^th place

acm.org

dl.acm.org

Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu e Tat-Seng Chua, Neural Collaborative Filtering, in Proceedings of the 26th International Conference on World Wide Web, International World Wide Web Conferences Steering Committee, 2017, pp. 173-182, DOI:10.1145/3038912.3052569. URL consultato il 16 ottobre 2019.
Maurizio Ferrari Dacrema, Paolo Cremonesi e Dietmar Jannach, Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches, in Proceedings of the 13th ACM Conference on Recommender Systems, ACM, 2019, pp. 101-109, DOI:10.1145/3298689.3347058. URL consultato il 16 ottobre 2019.
Malte Ludewig, Noemi Mauro, Sara Latifi e Dietmar Jannach, Performance Comparison of Neural and Non-neural Approaches to Session-based Recommendation, in Proceedings of the 13th ACM Conference on Recommender Systems, ACM, 2019, pp. 462-466, DOI:10.1145/3298689.3347041. URL consultato il 16 ottobre 2019.

arxiv.org

Ryan Prescott Adams, George E. Dahl e Iain Murray, Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes, in arXiv:1003.4944 [cs, stat], 25 marzo 2010.

doi.org

dx.doi.org

Yehuda Koren, Robert Bell e Chris Volinsky, Matrix Factorization Techniques for Recommender Systems, in Computer, vol. 42, n. 8, agosto 2009, pp. 30-37, DOI:10.1109/MC.2009.263.
Deepak Agarwal e Bee-Chung Chen, Regression-based latent factor models, ACM, 28 giugno 2009, pp. 19-28, DOI:10.1145/1557019.1557029.
(EN) Dietmar Jannach, Lukas Lerche, Fatih Gedikli e Geoffray Bonnin, What Recommenders Recommend – An Analysis of Accuracy, Popularity, and Sales Diversity Effects, in User Modeling, Adaptation, and Personalization, Springer Berlin Heidelberg, 2013, pp. 25-37, DOI:10.1007/978-3-642-38844-6_3.
(EN) Jian Cao, Hengkui Hu, Tianyan Luo, Jia Wang, May Huang, Karl Wang, Zhonghai Wu e Xing Zhang, Distributed Design and Implementation of SVD++ Algorithm for E-commerce Personalized Recommender System, in Communications in Computer and Information Science, Springer Singapore, 2015, pp. 30-44, DOI:10.1007/978-981-10-0421-6_4.
Yancheng Jia, Users' brands preference based on SVD++ in recommender systems, in 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA), IEEE, settembre 2014, DOI:10.1109/wartia.2014.6976489.
Daniel Kluver e Joseph A. Konstan, Evaluating recommender behavior for new users, ACM, 6 ottobre 2014, pp. 121-128, DOI:10.1145/2645710.2645742.
Yong Zheng, Bamshad Mobasher e Robin Burke, CSLIM: contextual SLIM recommendation algorithms, ACM, 6 ottobre 2014, pp. 301-304, DOI:10.1145/2645710.2645756.
Li Pu e Boi Faltings, Understanding and improving relational matrix factorization in recommender systems, ACM, 12 ottobre 2013, pp. 41-48, DOI:10.1145/2507157.2507178.
Changwei Zhao, Suhuan Sun, Linqian Han e Qinke Peng, HYBRID MATRIX FACTORIZATION FOR RECOMMENDER SYSTEMS IN SOCIAL NETWORKS, in Neural Network World, vol. 26, n. 6, 2016, pp. 559-569, DOI:10.14311/NNW.2016.26.032.
Tinghui Zhou, Hanhuai Shan, Arindam Banerjee e Guillermo Sapiro, Kernelized Probabilistic Matrix Factorization: Exploiting Graphs and Side Information, in Proceedings of the 2012 SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics, 26 aprile 2012, pp. 403-414, DOI:10.1137/1.9781611972825.35.
Yi Fang e Luo Si, Matrix co-factorization for recommendation with rich side information and implicit feedback, ACM, 27 ottobre 2011, pp. 65-69, DOI:10.1145/2039320.2039330.
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu e Tat-Seng Chua, Neural Collaborative Filtering, in Proceedings of the 26th International Conference on World Wide Web, International World Wide Web Conferences Steering Committee, 2017, pp. 173-182, DOI:10.1145/3038912.3052569. URL consultato il 16 ottobre 2019.
Maurizio Ferrari Dacrema, Paolo Cremonesi e Dietmar Jannach, Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches, in Proceedings of the 13th ACM Conference on Recommender Systems, ACM, 2019, pp. 101-109, DOI:10.1145/3298689.3347058. URL consultato il 16 ottobre 2019.
Malte Ludewig, Noemi Mauro, Sara Latifi e Dietmar Jannach, Performance Comparison of Neural and Non-neural Approaches to Session-based Recommendation, in Proceedings of the 13th ACM Conference on Recommender Systems, ACM, 2019, pp. 462-466, DOI:10.1145/3298689.3347041. URL consultato il 16 ottobre 2019.

mimuw.edu.pl

Arkadiusz Paterek, Improving regularized singular value decomposition for collaborative filtering (PDF), in Proceedings of KDD cup and workshop, 2007.

oadoi.org

Yehuda Koren, Robert Bell e Chris Volinsky, Matrix Factorization Techniques for Recommender Systems, in Computer, vol. 42, n. 8, agosto 2009, pp. 30-37, DOI:10.1109/MC.2009.263.
Deepak Agarwal e Bee-Chung Chen, Regression-based latent factor models, ACM, 28 giugno 2009, pp. 19-28, DOI:10.1145/1557019.1557029.
(EN) Dietmar Jannach, Lukas Lerche, Fatih Gedikli e Geoffray Bonnin, What Recommenders Recommend – An Analysis of Accuracy, Popularity, and Sales Diversity Effects, in User Modeling, Adaptation, and Personalization, Springer Berlin Heidelberg, 2013, pp. 25-37, DOI:10.1007/978-3-642-38844-6_3.
(EN) Jian Cao, Hengkui Hu, Tianyan Luo, Jia Wang, May Huang, Karl Wang, Zhonghai Wu e Xing Zhang, Distributed Design and Implementation of SVD++ Algorithm for E-commerce Personalized Recommender System, in Communications in Computer and Information Science, Springer Singapore, 2015, pp. 30-44, DOI:10.1007/978-981-10-0421-6_4.
Yancheng Jia, Users' brands preference based on SVD++ in recommender systems, in 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA), IEEE, settembre 2014, DOI:10.1109/wartia.2014.6976489.
Daniel Kluver e Joseph A. Konstan, Evaluating recommender behavior for new users, ACM, 6 ottobre 2014, pp. 121-128, DOI:10.1145/2645710.2645742.
Yong Zheng, Bamshad Mobasher e Robin Burke, CSLIM: contextual SLIM recommendation algorithms, ACM, 6 ottobre 2014, pp. 301-304, DOI:10.1145/2645710.2645756.
Li Pu e Boi Faltings, Understanding and improving relational matrix factorization in recommender systems, ACM, 12 ottobre 2013, pp. 41-48, DOI:10.1145/2507157.2507178.
Changwei Zhao, Suhuan Sun, Linqian Han e Qinke Peng, HYBRID MATRIX FACTORIZATION FOR RECOMMENDER SYSTEMS IN SOCIAL NETWORKS, in Neural Network World, vol. 26, n. 6, 2016, pp. 559-569, DOI:10.14311/NNW.2016.26.032.
Tinghui Zhou, Hanhuai Shan, Arindam Banerjee e Guillermo Sapiro, Kernelized Probabilistic Matrix Factorization: Exploiting Graphs and Side Information, in Proceedings of the 2012 SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics, 26 aprile 2012, pp. 403-414, DOI:10.1137/1.9781611972825.35.
Yi Fang e Luo Si, Matrix co-factorization for recommendation with rich side information and implicit feedback, ACM, 27 ottobre 2011, pp. 65-69, DOI:10.1145/2039320.2039330.

sifter.org

Simon Funk, FunkSVD proposal, su sifter.org.