Collaborative Filtering with Stacked Denoising AutoEncoders and Sparse Inputs

By Florian Strub, Jérémie Mary. 2015

169 citations recorded by the source corpus.

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. Yi Tay
4. Aminu Da’u
5. Fuli Feng
6. Tat-seng Chua
7. Wayne Xin Zhao
8. Liqiang Nie

Citations in the corpus

• Neural Collaborative Filtering (2017, methodology): “In contrast to the DAE-based CF [35], CDAE additionally plugs a user node to the input of autoencoders for reconstructing the user’s ratings.”
• Denoising Implicit Feedback for Recommendation (2020, background): “te the robustness of recommender systems under various attack methods, such as shilling attacks [17] and fuzzing attacks [29]. To build more robust recommender systems, some auto-encoder based models [20, 30, 33] introduce the denoising techniques. These approaches [33] firstcorrupttheinput,i.e.,the”
• A review on deep learning for recommender systems: challenges and remedies (2018, methodology): “…(2014) Li et al. (2015) Georgiev and Nakov (2013) Elkahky et al. (2015) Jia et al. (2015) Sedhain et al. (2015) Shin et al. (2015) Lei et al. (2016) Strub and Mary (2015) Zhou et al. (2016) Covington et al. (2016) Du et al. (2016) Shen et al. (2016) Strub et al. (2016) Zheng et al. (2016b) Zhang…”
• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, unknown): “decoder part [27], [28], [29], [30], [52], [53], [54].”
• Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning (2022, methodology): “Different from traditional CF methods, such as matrix factorization-based methods [13, 22] and auto-encoder-based methods [15, 25], graph-based collaborative filtering organize interaction data into an interaction graph and learn meaningful node representations from the graph structure information.”
• Deep Learning Based Recommender System (2017, methodology): “on models. Table 1. A lookup table for reviewed publications. Categories Publications MLP [2, 13, 20, 27, 38, 47, 53, 54, 66, 92, 95, 157, 166, 185], [12, 39, 93, 112, 134, 154, 182, 183] Autoencoder [34, 88, 89, 114, 116, 125, 136, 137, 140, 159, 177, 187, 207], [4, 10, 32, 94, 150, 151, 158, 170, ”
• Recommendation system based on deep learning methods: a systematic review and new directions (2019, background): “For example, Bansal et al. (2016), Soh et al. (2017) and Strub et al. (2016), explore to apply deeper architectures by adding more layers as their future direction.”
• Neural Networks and Deep Learning (2018, unknown): “The use of de-noising autoencoders in recommender systems is discussed in [472, 535].”