Probabilistic Matrix Factorization with Non-random Missing Data

By José Miguel Hernández-Lobato, Neil Houlsby, Zoubin Ghahramani. 2014

191 citations recorded by the source corpus.

How researchers see this paper

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

1. Xiangnan He
2. Xiang Wang
3. Tat-seng Chua
4. Fuli Feng
5. Meng Wang
6. Qiang Yang
7. Jiawei Chen
8. Chen Gao

Citations in the corpus

• Recommendations as Treatments: Debiasing Learning and Evaluation (2016, methodology): “Note that our performance numbers for HL on Yahoo closely match the values reported in (Hernández-Lobato et al., 2014).”
• Partially Observable Markov Decision Process for Recommender Systems (2016, background): “On one hand, the researchers proposed to augment the training data by generating negative samples [15, 11].”
• In2Rec: Influence-based Interpretable Recommendation (2019, methodology): “[12] presented the first practical implementation of a probabilistic matrix factorization model for MNAR data.”
• Neural Network Matrix Factorization (2015, background): “Some recent work by Hernández-Lobato et al. (2014) demonstrates that explicitly modeling the non-random pattern of missing ratings can lead to a slight improvement in performance for latent feature models, although the gains they demonstrated were not dramatic enough that they would have closed…”
• A Nonnegative Latent Factor Model for Large-Scale Sparse Matrices in Recommender Systems via Alternating Direction Method (2016, background): “In recent years, further research yielded more sophisticated models, such as the multidimensional probabilistic model [17], weighted trace-norm regularization-based model [18], collaborative Gaussian process-based preference model [19], nonparametric Bayesian-based latent factor model [20], and prob”
• Bias and Debias in Recommender System: A Survey and Future Directions (2020, methodology): “Training Data imputation [4], [20], [21], [63], [64], [65], [66] Propensity Score [62] Doubly robust model [67] Meta learning [3] Conformity Bias Modeling popularity influence [23] Modeling social influence [25], [26], [27], [28]”
• Advances and Challenges in Conversational Recommender Systems: A Survey (2021, background): “[66] analyze the phenomenon of popularity bias [156, 133] and selection bias [113, 61, 157] in simulators built on logged interaction data and try to alleviate model performance degradation due to these biases; it remains to be seen to which degree generated interactions of the type described above ”