Improving maximum margin matrix factorization

By Markus Weimer, Alexandros Karatzoglou, Alex Smola. 2008

152 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Recommender Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Qiang Yang
2. Julian McAuley
3. Xiangnan He
4. Weike Pan
5. Yu Zhang
6. Steffen Rendle
7. Zhong Ming
8. Tat-seng Chua

In Computer Vision and Pattern Recognition

1. Qiang Yang
2. Weike Pan
3. Julian McAuley
4. Dacheng Tao
5. Irwin King
6. Jieping Ye
7. Zhong Ming
8. Lars Schmidt-Thieme

In Information Systems

1. Julian McAuley
2. Xiangnan He
3. Steffen Rendle
4. Weike Pan
5. Qiang Yang
6. Ruining He
7. Irwin King
8. Christoph Freudenthaler

In Recommender Systems

1. Julian McAuley
2. Xiangnan He
3. Qiang Yang
4. Weike Pan
5. Ruining He
6. Steffen Rendle
7. Lars Schmidt-Thieme
8. Christoph Freudenthaler

Citations in the corpus

• BPR: Bayesian Personalized Ranking from Implicit Feedback (2009, methodology): “[15] use the maximum margin matrix factorization method (MMMF) for ordinal ranking.”
• Recommender Systems (2016, unknown): “CoFiRank: This approach [624, 625] is optimized for maximizing the NDCG measure with the use of structured estimation methods.”
• An accelerated gradient method for trace norm minimization (2009, methodology): “To overcome this limitation, a number of algorithms have been developed recently (Rennie & Srebro, 2005; Weimer et al., 2008a; Weimer et al., 2008b; Cai et al., 2008; Ma et al., 2008).”
• MyMediaLite: a free recommender system library (2011, methodology): “[22]) have published their algorithms as free software.”
• Trace Norm Regularization: Reformulations, Algorithms, and Multi-Task Learning (2010, methodology): “Multi-task learning, gene expression pattern analysis, trace norm regularization, convex optimiza- tion, duality, semidefinite programming, proximal gradient method.”
• Leveraging Social Connections to Improve Personalized Ranking for Collaborative Filtering (2014, methodology): “• MMMF: This method [20] is an extension of Maximum Margin Matrix Factorization for item recommendation optimized by a soft hinge ranking loss.”
• Compressed knowledge transfer via factorization machine for heterogeneous collaborative recommendation (2015, background): “, explicit ratings in matrix factorization [28,31] and implicit feedbacks in Bayesian personalized ranking (BPR) [27].”
• Transfer Learning for Semisupervised Collaborative Recommendation (2016, methodology): “But most previous works on preference learning and personalized recommendation focus on exploiting users’ homogeneous behaviors such as ratings [Salakhutdinov and Mnih 2008; Weimer et al. 2008], browsings [Kabbur et al.”