Matrix factorization techniques for context aware recommendation

By Linas Baltrunas, Bernd Ludwig, Francesco Ricci. 2011

378 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Nan Wang
2. Hongning Wang
3. Yiling Jia
4. Yue Yin
5. Di Wang
6. Bin Guo
7. Zhu Wang
8. Mengting Wan

In Information Systems

1. Hongning Wang
2. Nan Wang
3. Yiling Jia
4. Yue Yin
5. Di Wang
6. Matthew Goldman
7. Bin Guo
8. Justin M. Rao

Citations in the corpus

• Modeling Consumer Preferences and Price Sensitivities from Large-Scale Grocery Shopping Transaction Logs (2017, methodology): “Matrix factorization techniques have been widely applied for recommender systems due to their accuracy and scalability [6, 16, 21, 35].”
• Recommender Systems (2016, neutral): “The work in [63] proposes a family of matrix-factorization methods for context-aware recommendation, although the approach discussed in this book is more general than the methods discussed in this family.”
• CityTransfer (2018, methodology): “4) Matrix Factorization (MF) , which only uses the SVD-based CF method to predict ratings [2,23,35].”
• Explainable Recommendation via Multi-Task Learning in Opinionated Text Data (2018, methodology): “Latent factor models, such as matrix factorization [4] and tensor factorization [16], have achieved great success in practical recommender systems.”