The multiple multiplicative factor model for collaborative filtering

By Benjamin M. Marlin, Richard S. Zemel. 2004

89 citations recorded by the source corpus.

Subfields: Artificial Intelligence, 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 Artificial Intelligence

1. Yoshua Bengio
2. Ruslan Salakhutdinov
3. Quoc V. Le
4. Sergey Levine
5. Max Welling
6. Kyunghyun Cho
7. Mohit Bansal
8. Percy Liang

In Information Systems

1. Geoffrey E. Hinton
2. Yehuda Koren
3. Xing Xie
4. Ruslan Salakhutdinov
5. Fuzheng Zhang
6. Andriy Mnih
7. Yoshua Bengio
8. Xiangnan He

Citations in the corpus

• DRN: A Deep Reinforcement Learning Framework for News Recommendation (2018, methodology): “The current methods can be generally categorized as content based methods [19, 22, 33], collaborative filtering based methods [11, 28, 34], and hybrid methods [12, 24, 25].”
• A Survey of Collaborative Filtering Techniques (2009, background): “Multiple multiplicative factor models (MMFs) are a class of causal, discrete latent variable models combining factor distributions multiplicatively and are able to readily accommodate missing data [105].”
• Bayesian probabilistic matrix factorization using Markov chain Monte Carlo (2008, background): “A variety of probabilistic factor-based models have been proposed (Hofmann, 1999; Marlin, 2004; Marlin & Zemel, 2004; Salakhutdinov & Mnih, 2008).”
• Fast maximum margin matrix factorization for collaborative prediction (2005, methodology): “A common approach to collaborative prediction is to fit a factor model to the data, and use it in order to make further predictions (Azar et al., 2001; Billsus & Pazzani, 1998; Hofmann, 2004; Marlin & Zemel, 2004; Canny, 2004).”
• Probabilistic Matrix Factorization (2007, background): “A variety of probabilistic factor-based models has been pro posed recently [2, 3, 4].”
• Exponential Family Harmoniums with an Application to Information Retrieval (2004, background): “…latent trait models, latent Dirichlet allocation (LDA, otherwise known as multinomial PCA, or mPCA) [1], exponential family PCA (ePCA), probabilistic latent semantic indexing (pLSI) [6], non-negative matrix factorization (NMF), and more recently the multiple multiplicative factor model (MMF) [8].”
• Collaborative prediction using ensembles of Maximum Margin Matrix Factorizations (2006, methodology): “A linear factor model is a simple, natural, and well-studied (e.g. (Billus & Pazzani, 1998), (Hofmann, 2004),(Marlin & Zemel, 2004)) approach to collaborative prediction: (cid:12)t the n -by- d ratings matrix by a low rank decomposition UV 0 , where U is n -by- k and V is d -by- k and k min( n; d ).”
• Restricted Boltzmann machines for collaborative filtering (2007, background): “A variety of models have recently been proposed for minimizing the loss corresponding to a speci(cid:12)c probabilistic model (Hofmann, 1999; Canny, 2002; Marlin & Zemel, 2004).”