Deep Collaborative Filtering via Marginalized Denoising Auto-encoder

By Sheng Li, Jaya Kawale, Yun Raymond Fu. 2015

483 citations recorded by the source corpus.

Subfields: Information Systems, Transportation, Signal Processing

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Citations in the corpus

Neural Collaborative Filtering (2017, background): “Recently, autoencoders have become a popular choice for building recommendation systems [32, 22, 35].”
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): “• DCF. Deep Collaborative Filtering (Li, Kawale, and Fu 2015) is a model which combines PMF with marginalized denoising stacked autoencoders to achieve recommendation.”
Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources (2017, methodology): “For example, [25, 38] adopted denoising auto-encoders for recommendation, [46] developed a neural autoregressive approach for collaborative filtering, and [19, 20] generalized matrix factorization and factorization machines for neural collaborative filtering.”
Recommendation system based on deep learning methods: a systematic review and new directions (2019, background): “Optimization As a future direction, Many authors intend to use distributed optimization algorithms to further reduce the computational costs of their method algorithms (Li et al. 2015).”
Towards Bayesian Deep Learning: A Survey (2016, methodology): “It is therefore desirable to extend CDL to model user attributes as well [35].”
MMALFM (2018, methodology): “For example, Refs [40, 61] use auto-encoder approach for top-n recommendation.”
Deep Models of Interactions Across Sets (2018, methodology): “To our knowledge this is also true for all recent deep factorization and other collaborative ﬁltering techniques ( e.g. , Salakhut-dinov et al., 2007; Deng et al.; Sedhain et al., 2015; Wang et al., 2015; Li et al., 2015; Zheng et al., 2016; Dziugaite & Roy, 2015).”
Deep Matrix Factorization Models for Recommender Systems (2017, methodology): “Autoencoders and the denoising autoencoders have also been applied for recommendation [Li et al., 2015; Sedhain et al., 2015; Strub and Mary, 2015].”
Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation (2017, unknown): “Recently, autoencoders have also been successfully adopted for learning user representations based on rated items [23, 36, 44].”
Diffusion Recommender Model (2023, background): “Besides, generative models are specialized to capture the complex and non-linear relations between user preference and interactions as detailed in [19, 20, 36].”
When Recurrent Neural Networks meet the Neighborhood for Session-Based Recommendation (2017, methodology): “, in content-based approaches, in variations of latent factor models, or are part of new methods for computing recommendations [6, 7, 10, 22, 29, 33, 35].”
A General Cross-Domain Recommendation Framework via Bayesian Neural Network (2018, background): “So recently some hybrid recommendation systems adopt deep neural networks to learn latent representation from auxiliary information and have achieved excellent performance in single-domain recommendation tasks [5], [13]–[16].”
A review on deep learning for recommender systems: challenges and remedies (2018, methodology): “Wang et al. (2015b) utilize Bayesian SDAE whereas Li et al. (2015) utilize marginalized DAE to integrate auxiliary information into their recommender systems.”
Augmented Variational Autoencoders for Collaborative Filtering with Auxiliary Information (2017, methodology): “[22] used autoencoders to incorporate auxiliary information for both”
A Survey on Bayesian Deep Learning (2016, background): “Systems Collaborative Deep Learning (CDL) [121] HV Bayesian CDL [121] HV Marginalized CDL [66] LV Symmetric CDL [66] LV Collaborative Deep Ranking [131] HV Collaborative Knowledge Base Embedding [132] HV Collaborative Recurrent AE [122] HV Collaborative Variational Autoencoders [68] HV”
MIDA: Multiple Imputation Using Denoising Autoencoders (2017, methodology): “Denoising autoencoders have been recently used in completing traffic and health records data [5,1] and collaborative filtering [8].”
Deep Item-based Collaborative Filtering for Top-N Recommendation (2018, methodology): “Autoencoders and the denoising autoencoders have already applied for recommendation based on explicit feedback [21, 33].”