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Deep Autoencoder for Recommender Systems: Parameter Influence Analysis [article]

Dai Hoang Tran, Zawar Hussain, Wei Emma Zhang, Nguyen Lu Dang Khoa, Nguyen H. Tran, Quan Z. Sheng
2018 arXiv   pre-print
The state-of-the-art deep neural network models used in recommender systems are typically multilayer perceptron and deep Autoencoder (DAE), among which DAE usually shows better performance due to its superior  ...  Suzuki and Ozaki (2017) applied a hybrid model using both Autoencoder model and collaborative filtering method to calculate the hidden similarity for more serendipitous recommendation.  ...  The first one is Collaborative Filtering (CF) (Bell and Koren 2007) , in which each user is recommended with items based on other users with similar preferences.  ... 
arXiv:1901.00415v1 fatcat:7uq6ogltc5altkard7aoid5ska

Deep Autoencoder for Recommender Systems: Parameter Influence Analysis [chapter]

Dai Hoang Tran, Macquarie University, AU, Zawar Hussain, Wei Emma Zhang, Nguyen Lu Dang Khoa, Nguyen H. Tran, Quan Z. Sheng, Macquarie University, AU, Macquarie University, AU, CSIRO Data61, AU, The University of Sydney, AU, Macquarie University, AU
2018 Australasian Conference on Information Systems 2018  
The state-of-the-art deep neural network models used in recommender systems are multilayer perceptron and deep autoencoder (DAE).  ...  Suzuki and Ozaki (2017) applied a hybrid model using both autoencoder model and collaborative filtering method to calculate the hidden similarity for more serendipitous recommendation.  ...  The first one is Collaborative Filtering (CF) (Bell and Koren 2007) , in which each user is recommended with items based on other users with similar preferences.  ... 
doi:10.5130/acis2018.aj fatcat:hcw7bkjcxvafbanamy7ggpiwfa

Why Not to Use Zero Imputation? Correcting Sparsity Bias in Training Neural Networks

Joonyoung Yi, Juhyuk Lee, Kwang Joon Kim, Sung Ju Hwang, Eunho Yang
2020 arXiv   pre-print
Autorec: Autoencoders meet collaborative filtering. In Proceedings of the 24th International Conference on World Wide Web, pp. 111-112. ACM, 2015. I. Silva, George B. Moody, Daniel J. Scott, L.  ...  Factorization meets the neighborhood: a multifaceted collaborative filtering model.  ... 
arXiv:1906.00150v5 fatcat:uzeeojwbejbyrfnx6yd65lpub4

Recommendation System Using Autoencoders

Diana Ferreira, Sofia Silva, António Abelha, José Machado
2020 Applied Sciences  
In this article, a product recommendation system is proposed where an autoencoder based on a collaborative filtering method is employed.  ...  Collaborative filtering is widely used in this type of systems, but high dimensions and data sparsity are always a main problem.  ...  Suvash et al. propose AutoRec, a novel autoencoder framework for collaborative filtering (CF).  ... 
doi:10.3390/app10165510 fatcat:youna3oxdvhjripm4vmj7ven6e

Implicit Feedback Deep Collaborative Filtering Product Recommendation System [article]

Karthik Raja Kalaiselvi Bhaskar, Deepa Kundur, Yuri Lawryshyn
2020 arXiv   pre-print
In this paper, several Collaborative Filtering (CF) approaches with latent variable methods were studied using user-item interactions to capture important hidden variations of the sparse customer purchasing  ...  CF with Neural Collaborative Filtering(NCF) was shown to produce the highest Normalized Discounted Cumulative Gain (NDCG) performance on the real-world proprietary dataset provided by a large parts supply  ...  Matrix Factorization with Alternating Least Square (ALS) [10] , Bayesian Personalized Ranking (BPR) [12] , Neural Collaborative Filtering (NCF) [11] and Autoencoder for Collaborative Filtering (ACF  ... 
arXiv:2009.08950v2 fatcat:h6jysprlhzckjoo2kfccs72rxi

Deep Learning Enabled Autoencoder Architecture for Collaborative Filtering Recommendation in IoT Environment

Thavavel Vaiyapuri
2021 Computers Materials & Continua  
Most of the existing techniques-including collaborative filtering (CF), which is most widely adopted when building recommendation systems-suffer from rating sparsity and cold-start problems, preventing  ...  Inspired by the great success of deep learning in a wide range of fields, this work introduces a deep-learning-enabled autoencoder architecture to overcome the setbacks of CF recommendations.  ...  Preliminaries Neural Collaborative Filtering (NCF) NCF is an extended version of a traditional CF model.  ... 
doi:10.32604/cmc.2021.015998 fatcat:rgzdbm2xwjhrjmasbxdud6dv7m

The evolution of travel recommender systems: A comprehensive review

Muneer V. K., K. P. Mohamed Basheer
2020 Malaya Journal of Matematik  
S.Sedhain [38] introduced an innovative autoencoder frame-work, to deal with explicit neural models, AutoRec, for collaborative filtering (CF).  ...  Collaborative Filtering (CF) Collaborative filtering is a technique that filters information for different sets of data using different collaboration techniques.  ... 
doi:10.26637/mjm0804/0075 fatcat:x2f34v67hfg75le7tquws3siem

Developing Emotion-Aware Human–Robot Dialogues for Domain-Specific and Goal-Oriented Tasks

Jhih-Yuan Huang, Wei-Po Lee, Chen-Chia Chen, Bu-Wei Dong
2020 Robotics  
One of the popular autoencoder-based recommendation models is AutoRec [42] .  ...  One of the popular autoencoder-based recommendation models is AutoRec [42] .  ... 
doi:10.3390/robotics9020031 fatcat:trwmou2gbfg7ffldwcuqtnmo7y

Fast Multi-Step Critiquing for VAE-based Recommender Systems [article]

Diego Antognini, Boi Faltings
2021 arXiv   pre-print
We address these deficiencies with M&Ms-VAE, a novel variational autoencoder for recommendation and explanation that is based on multimodal modeling assumptions.  ...  VAE-CF is the variational autoencoder for collaborative filtering described in Section 2.2.  ...  CE-VNCF [45] is the extension of the neural collaborative filtering model [15] that is augmented with an explanation and a critiquing neural component.  ... 
arXiv:2105.00774v2 fatcat:ffddagvvbzd2xcsutiwfsxns54

Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network

Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Bo Zhang, Liefeng Bo
2020 Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval  
Finally, a cross-behavior aggregation component is introduced to promote the comprehensive collaboration across type-aware interaction behavior representations, and discriminate their inherent contributions  ...  Collaborative Filtering with Auto-Encoder: • AutoRec [27] : It leverages a three-layer autoencoder to map user-item interactions into latent representations. • CDAE [45] : In this autoencoder CF, an  ...  to supercharge collaborative filtering with non-linear neural networks.  ... 
doi:10.1145/3397271.3401445 dblp:conf/sigir/XiaHXDZB20 fatcat:vn6dqjgycfhtfixke6g4m554ie

A Survey of Online Course Recommendation Techniques

Jinliang Lu
2022 Open Journal of Applied Sciences  
The recommender system has been widely used in various Internet applications due to its high efficiency in filtering information, helping users to quickly find personalized resources from thousands of  ...  At present, there are many researches on collaborative filtering.  ...  Referring to the idea of autoencoder, AutoRec [19] and CDAE [20] use neural networks to implement encoders and decoders to reconstruct the user item interaction matrix.  ... 
doi:10.4236/ojapps.2022.121010 fatcat:ww7vgve2bvecjfjyzc2iqevcam

Graph Meta Network for Multi-Behavior Recommendation [article]

Lianghao Xia, Yong Xu, Chao Huang, Peng Dai, Liefeng Bo
2021 arXiv   pre-print
Autoencoder-based Collaborative Filtering Models: • CDAE [34] : it applies the denoising autoencoders to train a neural network with the data reconstruction objective in an adaptive way. • AutoRec [25  ...  Autoencoder and its model variants have also been utilized for collaborative filtering tasks with the reconstruction-based encoderdecoder learning over user-item interactions [25, 34] .  ... 
arXiv:2110.03969v1 fatcat:w4mrfiyvabgsvhcu7jzototitu

RMPD: Method for enhancing the robustness of recommendations with attack environments

Qi Ding, Peiyu Liu, Zhenfang Zhu, Huajuan Duan, Fuyong Xu
2021 IEEE Access  
[19] proposed collaborative filtering method based on autoencoder that via decoding process to output the rows or columns of the input rating matrix, and pass the minimum optimization of model parameters  ...  [19] : A collaborative filtering method based on AutoEncoder is proposed to solve the predictive rating method of the user-item rating matrix in the recommendation system. • GraphRfi [31] : A user representation  ... 
doi:10.1109/access.2021.3054122 fatcat:62z42ezqijbfrk2ckofnsthfyu

GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection

Shijie Zhang, Hongzhi Yin, Tong Chen, Quoc Viet Nguyen Hung, Zi Huang, Lizhen Cui
2020 Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval  
CCS CONCEPTS • Information systems → Collaborative and social computing systems and tools.  ...  AutoRec [41] : This is a recent recommendation model that fuses collaborative filtering with autoencoders.  ...  [41, 47] consider the collaborative filtering from Autoencoder perspective, while [3, 9, 54] utilize graph convolutional networks for feature extraction.  ... 
doi:10.1145/3397271.3401165 dblp:conf/sigir/ZhangYCHHC20 fatcat:bbsrgjy2nbfrbcey34ai2e33yq

GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection [article]

Shijie Zhang, Hongzhi Yin, Tong Chen, Quoc Viet Nguyen Hung, Zi Huang, Lizhen Cui
2020 arXiv   pre-print
AutoRec [41] : This is a recent recommendation model that fuses collaborative filtering with autoencoders.  ...  [41, 47] consider the collaborative filtering from Autoencoder perspective, while [3, 9, 54] utilize graph convolutional networks for feature extraction.  ... 
arXiv:2005.10150v1 fatcat:fqiafabci5fyhd2wquzxlyepsm
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