Logistic Matrix Factorization for Implicit Feedback Data

By Christopher C. Johnson. 2014

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. Fuli Feng
4. Meng Wang
5. Le Wu
6. Jiawei Chen
7. Chen Gao
8. Tat-seng Chua

Citations in the corpus

• Neural Networks and Deep Learning (2018, unknown): “However, recommender systems with implicit feedback use logistic matrix factorization [224], which is similar to the word2vec setting.”
• Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback (2019, methodology): “Using logistic matrix factorization [13], we found an approximation of the true observations as”
• Bias and Debias in Recommender System: A Survey and Future Directions (2020, methodology): “For example, Johnson et al. [78] assumes an interaction can be estimated by embedding inner product, while He et al. [64] adopts the neural network as its better generalization.”
• Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction (2016, methodology): “(7) can be solved using an alternating gradient descent method [35].”
• Meta-Prod2Vec: Product Embeddings Using Side-Information for Recommendation (2016, methodology): “The latter models the probability that a user would interact with an item by replacing the square loss in MF method with the logistic loss [11].”
• Item recommendation on monotonic behavior chains (2018, methodology): “• logMF [10].”