A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines

By Kostadin Georgiev, Preslav Nakov. 2013

162 citations recorded by the source corpus.

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In Recommender Systems

Citations in the corpus

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): “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].”
Explainable Recommendation: A Survey and New Perspectives (2018, background): “Because this survey focuses on explainable recommendation, readers are are suggested to refer Gunawardana and Shani [2009] and Beel et al.”
Recommendation system based on deep learning methods: a systematic review and new directions (2019, methodology): “Georgiev et al. (2013) proposed a combined user-based and item-based RBM-CFs in a unified framework.”
Towards Bayesian Deep Learning: A Survey (2016, methodology): “[49] uses restricted Boltzmann machines instead of the conventional matrix factorization formulation to perform CF and [17] extends this work by incorporating user-user and item-item correlations.”
Deep Learning Based Recommender System (2017, methodology): “[30] proposed to combine the user-based and item-based RBM in a unied framework.”
A Survey on Bayesian Deep Learning (2016, methodology): “[98] uses restricted Boltzmann machines instead of the conventional matrix factorization formulation to perform CF and [28] extends this work by incorporating user-user and item-item correlations.”
A Context-Aware User-Item Representation Learning for Item Recommendation (2017, methodology): “Neighborhood information of users and items is then utilized to extend the RBM model for better prediction performance [18].”
Collaborative Variational Autoencoder for Recommender Systems (2017, methodology): “[7, 23] use restricted Boltzmann machines instead of the conventional matrix factorization to perform collaborative ltering (CF).”
Deep Collaborative Filtering via Marginalized Denoising Auto-encoder (2015, methodology): “Our mDA-CF and mSDA-CF approaches obtain lower MAE than UI-RBM, demonstrating the effectiveness of our DCF framework compared to the RBM based deep learning models.”
Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks (2016, methodology): “[16, 6, 7] use restricted Boltzmann machines and RNN instead of the conventional matrix factorization (MF) formulation to perform CF.”
Structural Deep Network Embedding (2016, methodology): “In [9], Restricted Boltzmann Machines were adopted to do collaborative filtering.”
A review on deep learning for recommender systems: challenges and remedies (2018, methodology): “However, using Boltzmann machines to model the pairwise user or item correlations and neighborhood formation performs with higher accuracy (Georgiev and Nakov 2013).”