Restricted Boltzmann machines for collaborative filtering

By Ruslan Salakhutdinov, Andriy Mnih, Geoffrey E. Hinton. 2007

2,094 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Probabilistic & Bayesian Methods, Recommender Systems, Signal Processing

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. Luc Van Gool
2. Dacheng Tao
3. Trevor Darrell
4. Ming-Hsuan Yang
5. Xiaogang Wang
6. Jitendra Malik
7. Yi Yang
8. Andrea Vedaldi

In Information Systems

1. Xiangnan He
2. Yongfeng Zhang
3. Xu Chen
4. Xiang Wang
5. Tat-seng Chua
6. Charu C. Aggarwal
7. Julian McAuley
8. Xing Xie

In Probabilistic & Bayesian Methods

1. Yoshua Bengio
2. David M. Blei
3. Aaron C. Courville
4. Ruslan Salakhutdinov
5. Zoubin Ghahramani
6. Pascal Vincent
7. Kyunghyun Cho
8. Geoffrey E. Hinton

In Recommender Systems

1. Xiangnan He
2. Yongfeng Zhang
3. Julian McAuley
4. Hao Wang
5. Yehuda Koren
6. Tat-seng Chua
7. Dit-Yan Yeung
8. Harald Steck

In Signal Processing

1. Yoshua Bengio
2. Charu C. Aggarwal
3. David M. Blei
4. Li Deng
5. Dong Yu
6. Ruslan Salakhutdinov
7. Geoffrey E. Hinton
8. Dit-Yan Yeung

Citations in the corpus

• VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models (2020, background): “, 2016b), and energy-based models (EBMs) (Lecun et al., 2006; Salakhutdinov et al., 2007).”
• High Fidelity Visualization of What Your Self-Supervised Representation Knows About (2021, neutral): “These include probabilistic latent variable models and variants of auto-encoders (Ack-ley et al., 1985; Hinton et al., 2006; Salakhutdinov et al., 2007; Vincent et al., 2008; Kingma & Welling, 2014; Rezende et al., 2014), that are traditionally put under the broad umbrella term of unsupervised…”
• Collaborative Deep Learning for Recommender Systems (2014, methodology): “[28] uses restricted Boltzmann machines instead of the conventional matrix factorization formulation to perform CF and [9] extends this work by incorporating user-user and item-item correlations.”
• Variational Autoencoders for Collaborative Filtering (2018, background): “es for β. Neural networks for collaborative filtering.Early work on neural-network-based collaborative filtering models focus on explicit feedback data and evaluates on the task of rating predictions [11, 39, 41, 54]. The importance of implicit feedback has been gradually recognized, and consequentl”
• Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “RBMs are used in applications such as dimension reduction, classification, feature learning, collaborative filtering [67].”
• Recurrent Latent Variable Networks for Session-Based Recommendation (2017, background): “In this vein, the work of [28] constitutes one of the earliest”
• Neural Collaborative Filtering (2017, background): “[30] proposed a two-layer Restricted Boltzmann Machines (RBMs) to model users’ explicit ratings on items.”
• Improved Recurrent Neural Networks for Session-based Recommendations (2016, methodology): “They have also been used for collaborative filtering [29, 21].”
• A Neural Autoregressive Approach to Collaborative Filtering (2016, methodology): “A prominent example is RBM-based CF (RBM-CF) (Salakhutdinov et al., 2007).”
• Session-based Recommendations with Recurrent Neural Networks (2015, neutral): “This model has been shown to be one of the best performing Collaborative Filtering models.”
• Joint Topic-Semantic-aware Social Recommendation for Online Voting (2017, methodology): “In addition, various models are incorporated into collaborative filtering, such as Support Vector Machine [30], Restricted Boltzmann Machine [24], and Stacked Denoising Auto Encoder [27].”
• Online Contrastive Divergence with Generative Replay: Experience Replay without Storing Data (2016, background): “Examples of these applications are: modeling human choice (Osogami and Otsuka, 2014), collaborative filtering (Salakhutdinov et al, 2007), information retrieval (Gehler et al, 2006), transfer learning (Ammar et al, 2013), or multi-class classification (Larochelle and Bengio, 2008).”
• Ask the GRU: Multi-task Learning for Deep Text Recommendations (2016, methodology): “[36] used restricted Boltzmann machines as one of the component models to tackle the Netflix challenge.”
• Deep Learning of Representations: Looking Forward (2013, methodology): “A deep learning building block (the restricted Boltzmann machine, or RBM) was used as a crucial part of the winning entry of a million-dollar machine learning competition (the Netflix competition) (Salakhutdinov et al., 2007; Töscher et al., 2009).”
• Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science (2017, neutral): “13,25 using more complex approaches to remove unimportant weights.”
• DeepFM: A Factorization-Machine based Neural Network for CTR Prediction (2017, neutral): “[ Salakhutdinov et al. , 2007; Sedhain et al. , 2015; Wang et al. , 2015 ]”
• Joint Deep Modeling of Users and Items Using Reviews for Recommendation (2017, background): “Several works [26, 37, 15] model users and/or items from the rating matrix using neural networks like denoising auto-encoders or Restricted Boltzmann Machines (RBM).”
• A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems (2017, methodology): “Work (Salakhutdinov, Mnih, and Hinton 2007) employs restricted Boltzmann machines to perform CF.”