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Matrix factorization (recommender systems) (English Wikipedia)

Analysis of information sources in references of the Wikipedia article "Matrix factorization (recommender systems)" in English language version.

refsWebsite
Global rank English rank
18doi.org
2nd place
2nd place
12semanticscholar.org
11th place
8th place
4arxiv.org
69th place
59th place
2psu.edu
207th place
136th place
1sifter.org
low place
low place
1tandfonline.com
507th place
429th place
1figshare.com
5,893rd place
3,320th place
1mimuw.edu.pl
low place
low place
1datajobs.com
low place
low place
1epfl.ch
4,521st place
4,877th place
1acm.org
1,185th place
840th place

acm.org

dl.acm.org

arxiv.org

datajobs.com

doi.org

  • Koren, Yehuda; Bell, Robert; Volinsky, Chris (August 2009). "Matrix Factorization Techniques for Recommender Systems". Computer. 42 (8): 30–37. CiteSeerX 10.1.1.147.8295. doi:10.1109/MC.2009.263. S2CID 58370896.
  • ChenHung-Hsuan; ChenPu (2019-01-09). "Differentiating Regularization Weights – A Simple Mechanism to Alleviate Cold Start in Recommender Systems". ACM Transactions on Knowledge Discovery from Data. 13: 1–22. doi:10.1145/3285954. S2CID 59337456.
  • Agarwal, Deepak; Chen, Bee-Chung (28 June 2009). "Regression-based latent factor models". Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining – KDD '09. ACM. pp. 19–28. doi:10.1145/1557019.1557029. ISBN 9781605584959. S2CID 17484284.
  • Jannach, Dietmar; Lerche, Lukas; Gedikli, Fatih; Bonnin, Geoffray (2013). "What Recommenders Recommend – an Analysis of Accuracy, Popularity, and Sales Diversity Effects". User Modeling, Adaptation, and Personalization. Lecture Notes in Computer Science. Vol. 7899. Springer Berlin Heidelberg. pp. 25–37. CiteSeerX 10.1.1.465.96. doi:10.1007/978-3-642-38844-6_3. ISBN 978-3-642-38843-9.
  • Bi, Xuan; Qu, Annie; Wang, Junhui; Shen, Xiaotong (2017). "A group-specific recommender system". Journal of the American Statistical Association. 112 (519): 1344–1353. doi:10.1080/01621459.2016.1219261. S2CID 125187672.
  • Zhu, Yunzhang; Shen, Xiaotong; Ye, Changqing (2016). "Personalized prediction and sparsity pursuit in latent factor models" (PDF). Journal of the American Statistical Association. 111 (513): 241–252. doi:10.1080/01621459.2016.1219261. S2CID 125187672.
  • Cao, Jian; Hu, Hengkui; Luo, Tianyan; Wang, Jia; Huang, May; Wang, Karl; Wu, Zhonghai; Zhang, Xing (2015). Distributed Design and Implementation of SVD++ Algorithm for E-commerce Personalized Recommender System. Communications in Computer and Information Science. Vol. 572. Springer Singapore. pp. 30–44. doi:10.1007/978-981-10-0421-6_4. ISBN 978-981-10-0420-9.
  • Jia, Yancheng (September 2014). "Users' brands preference based on SVD++ in recommender systems". 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA). IEEE. pp. 1175–1178. doi:10.1109/wartia.2014.6976489. ISBN 978-1-4799-6989-0. S2CID 742206.
  • Kluver, Daniel; Konstan, Joseph A. (6 October 2014). "Evaluating recommender behavior for new users". Proceedings of the 8th ACM Conference on Recommender systems – Rec Sys '14. ACM. pp. 121–128. doi:10.1145/2645710.2645742. ISBN 9781450326681. S2CID 18509558.
  • Zheng, Yong; Mobasher, Bamshad; Burke, Robin (6 October 2014). "CSLIM". CSLIM: contextual SLIM recommendation algorithms. ACM. pp. 301–304. doi:10.1145/2645710.2645756. ISBN 9781450326681. S2CID 15931532.
  • Pu, Li; Faltings, Boi (12 October 2013). "Understanding and improving relational matrix factorization in recommender systems". Proceedings of the 7th ACM conference on Recommender systems – Rec Sys '13. ACM. pp. 41–48. doi:10.1145/2507157.2507178. ISBN 9781450324090. S2CID 14106198.
  • Zhao, Changwei; Sun, Suhuan; Han, Linqian; Peng, Qinke (2016). "Hybrid Matrix Factorization for Recommender Systems in Social Networks". Neural Network World. 26 (6): 559–569. doi:10.14311/NNW.2016.26.032.
  • Zhou, Tinghui; Shan, Hanhuai; Banerjee, Arindam; Sapiro, Guillermo (26 April 2012). "Kernelized Probabilistic Matrix Factorization: Exploiting Graphs and Side Information". Proceedings of the 2012 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics. pp. 403–414. doi:10.1137/1.9781611972825.35. ISBN 978-1-61197-232-0.
  • Fang, Yi; Si, Luo (27 October 2011). "Matrix co-factorization for recommendation with rich side information and implicit feedback". Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems – Het Rec '11. ACM. pp. 65–69. doi:10.1145/2039320.2039330. ISBN 9781450310277. S2CID 13850687.
  • He, Xiangnan; Liao, Lizi; Zhang, Hanwang; Nie, Liqiang; Hu, Xia; Chua, Tat-Seng (2017). "Neural Collaborative Filtering". Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee. pp. 173–182. arXiv:1708.05031. doi:10.1145/3038912.3052569. ISBN 9781450349130. S2CID 13907106. Retrieved 16 October 2019.
  • Rendle, Steffen; Krichene, Walid; Zhang, Li; Anderson, John (22 September 2020). "Neural Collaborative Filtering vs. Matrix Factorization Revisited". Fourteenth ACM Conference on Recommender Systems. pp. 240–248. arXiv:2005.09683. doi:10.1145/3383313.3412488. ISBN 9781450375832.
  • Dacrema; Ferrari (2021). "A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research". ACM Transactions on Information Systems. 39 (2): 39.2. arXiv:1911.07698. doi:10.1145/3434185. S2CID 208138060.
  • Ludewig, Malte; Mauro, Noemi; Latifi, Sara; Jannach, Dietmar (2019). "Performance comparison of neural and non-neural approaches to session-based recommendation". Proceedings of the 13th ACM Conference on Recommender Systems. ACM. pp. 462–466. doi:10.1145/3298689.3347041. ISBN 9781450362436.

epfl.ch

infoscience.epfl.ch

figshare.com

mimuw.edu.pl

psu.edu

citeseerx.ist.psu.edu

  • Koren, Yehuda; Bell, Robert; Volinsky, Chris (August 2009). "Matrix Factorization Techniques for Recommender Systems". Computer. 42 (8): 30–37. CiteSeerX 10.1.1.147.8295. doi:10.1109/MC.2009.263. S2CID 58370896.
  • Jannach, Dietmar; Lerche, Lukas; Gedikli, Fatih; Bonnin, Geoffray (2013). "What Recommenders Recommend – an Analysis of Accuracy, Popularity, and Sales Diversity Effects". User Modeling, Adaptation, and Personalization. Lecture Notes in Computer Science. Vol. 7899. Springer Berlin Heidelberg. pp. 25–37. CiteSeerX 10.1.1.465.96. doi:10.1007/978-3-642-38844-6_3. ISBN 978-3-642-38843-9.

semanticscholar.org

api.semanticscholar.org

  • Koren, Yehuda; Bell, Robert; Volinsky, Chris (August 2009). "Matrix Factorization Techniques for Recommender Systems". Computer. 42 (8): 30–37. CiteSeerX 10.1.1.147.8295. doi:10.1109/MC.2009.263. S2CID 58370896.
  • ChenHung-Hsuan; ChenPu (2019-01-09). "Differentiating Regularization Weights – A Simple Mechanism to Alleviate Cold Start in Recommender Systems". ACM Transactions on Knowledge Discovery from Data. 13: 1–22. doi:10.1145/3285954. S2CID 59337456.
  • Agarwal, Deepak; Chen, Bee-Chung (28 June 2009). "Regression-based latent factor models". Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining – KDD '09. ACM. pp. 19–28. doi:10.1145/1557019.1557029. ISBN 9781605584959. S2CID 17484284.
  • Bi, Xuan; Qu, Annie; Wang, Junhui; Shen, Xiaotong (2017). "A group-specific recommender system". Journal of the American Statistical Association. 112 (519): 1344–1353. doi:10.1080/01621459.2016.1219261. S2CID 125187672.
  • Zhu, Yunzhang; Shen, Xiaotong; Ye, Changqing (2016). "Personalized prediction and sparsity pursuit in latent factor models" (PDF). Journal of the American Statistical Association. 111 (513): 241–252. doi:10.1080/01621459.2016.1219261. S2CID 125187672.
  • Jia, Yancheng (September 2014). "Users' brands preference based on SVD++ in recommender systems". 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA). IEEE. pp. 1175–1178. doi:10.1109/wartia.2014.6976489. ISBN 978-1-4799-6989-0. S2CID 742206.
  • Kluver, Daniel; Konstan, Joseph A. (6 October 2014). "Evaluating recommender behavior for new users". Proceedings of the 8th ACM Conference on Recommender systems – Rec Sys '14. ACM. pp. 121–128. doi:10.1145/2645710.2645742. ISBN 9781450326681. S2CID 18509558.
  • Zheng, Yong; Mobasher, Bamshad; Burke, Robin (6 October 2014). "CSLIM". CSLIM: contextual SLIM recommendation algorithms. ACM. pp. 301–304. doi:10.1145/2645710.2645756. ISBN 9781450326681. S2CID 15931532.
  • Pu, Li; Faltings, Boi (12 October 2013). "Understanding and improving relational matrix factorization in recommender systems". Proceedings of the 7th ACM conference on Recommender systems – Rec Sys '13. ACM. pp. 41–48. doi:10.1145/2507157.2507178. ISBN 9781450324090. S2CID 14106198.
  • Fang, Yi; Si, Luo (27 October 2011). "Matrix co-factorization for recommendation with rich side information and implicit feedback". Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems – Het Rec '11. ACM. pp. 65–69. doi:10.1145/2039320.2039330. ISBN 9781450310277. S2CID 13850687.
  • He, Xiangnan; Liao, Lizi; Zhang, Hanwang; Nie, Liqiang; Hu, Xia; Chua, Tat-Seng (2017). "Neural Collaborative Filtering". Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee. pp. 173–182. arXiv:1708.05031. doi:10.1145/3038912.3052569. ISBN 9781450349130. S2CID 13907106. Retrieved 16 October 2019.
  • Dacrema; Ferrari (2021). "A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research". ACM Transactions on Information Systems. 39 (2): 39.2. arXiv:1911.07698. doi:10.1145/3434185. S2CID 208138060.

sifter.org

tandfonline.com

amstat.tandfonline.com