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Gaussian Embeddings for Collaborative Filtering

Ludovic Dos Santos, Benjamin Piwowarski, Patrick Gallinari
2017 Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '17  
In this paper, we leverage recent works in learning Gaussian embeddings for the recommendation task.  ...  Most collaborative ltering systems, such as matrix factorization, use vector representations for items and users.  ...  The Gaussian Embeddings Ranking Model.  ... 
doi:10.1145/3077136.3080722 dblp:conf/sigir/SantosPG17 fatcat:e2ecb2gsqjcdtfww5rord7pe6y

Item Recommendation with Variational Autoencoders and Heterogenous Priors [article]

Giannis Karamanolakis, Kevin Raji Cherian, Ananth Ravi Narayan, Jie Yuan, Da Tang, Tony Jebara
2018 arXiv   pre-print
We extend VAEs to collaborative filtering with side information, for instance when ratings are combined with explicit text feedback from the user.  ...  Our proposed model is shown to outperform the existing VAE models for collaborative filtering (up to 29.41% relative improvement in ranking metric) along with other baselines that incorporate both user  ...  VAE for Collaborative Filtering (Mult-VAE). The conventional VAEs for collaborative filtering [24] . We use exactly the same architecture as in VAE-HPrior.  ... 
arXiv:1807.06651v1 fatcat:rduqkizfw5a33ej64ebpygc6zm

Deep Variational Embedding Representation on Neural Collaborative Filtering Recommender Systems

Jesús Bobadilla, Jorge Dueñas, Abraham Gutiérrez, Fernando Ortega
2022 Applied Sciences  
Current collaborative filtering machine learning models are designed to improve prediction accuracy, not to provide suitable visual representations of data.  ...  The proposed neural model incorporates variational embedding stages to "unpack" (extend) embedding representations, which facilitates identifying individual samples.  ...  Proposed models: Green: collaborative filtering prediction; Orange: variational Euclidean model for users; Blue: variational Euclidean model for items.  ... 
doi:10.3390/app12094168 fatcat:edqprjflajah7fzsshclox56ia

Personalizing Embedded Assessment Sequences in Narrative-Centered Learning Environments: A Collaborative Filtering Approach [chapter]

Wookhee Min, Jonathan P. Rowe, Bradford W. Mott, James C. Lester
2013 Lecture Notes in Computer Science  
We present an approach for personalizing embedded assessment sequences that is based on collaborative filtering.  ...  Using data from a multiweek classroom study with 850 students, we compare two model-based collaborative filtering methods, including probabilistic principal component analysis (PPCA) and non-negative matrix  ...  The authors wish to thank colleagues from the IntelliMedia Group and East Carolina University for their assistance. This research was supported  ... 
doi:10.1007/978-3-642-39112-5_38 fatcat:kkxifz27wbhpxkm4kkgvi7qkem

RecVAE: A New Variational Autoencoder for Top-N Recommendations with Implicit Feedback

Ilya Shenbin, Anton Alekseev, Elena Tutubalina, Valentin Malykh, Sergey I. Nikolenko
2020 Proceedings of the 13th International Conference on Web Search and Data Mining  
Recent research has shown the advantages of using autoencoders based on deep neural networks for collaborative filtering.  ...  In experimental evaluation, we show that RecVAE significantly outperforms previously proposed autoencoder-based models, including Mult-VAE and RaCT, across classical collaborative filtering datasets, and  ...  Autoencoders and Regularization for Collaborative Filtering Let U and I be the sets of users and items respectively in a collaborative filtering problem.  ... 
doi:10.1145/3336191.3371831 dblp:conf/wsdm/ShenbinATMN20 fatcat:uo5sftpsdzcmxjek6h27rxmile

Visualization of Collaborative Data [article]

Guobiao Mei, Christian R. Shelton
2012 arXiv   pre-print
Much of the work with such data focuses on filtering: predicting unknown ratings for pairs of users and items. In this paper we focus on the problem of visualizing the information.  ...  Collaborative data consist of ratings relating two distinct sets of objects: users and items.  ...  Acknowledgments We thank Titus Winters for collecting and sharing the SAT dataset. This work was supported in part by a grant from Intel Research and the UC MICRO program.  ... 
arXiv:1206.6850v1 fatcat:w6qfdh75wzexzpm7t5th5d7s4u

Dual Adversarial Variational Embedding for Robust Recommendation [article]

Qiaomin Yi, Ning Yang, Philip S. Yu
2021 arXiv   pre-print
In this paper, we propose a novel model called Dual Adversarial Variational Embedding (DAVE) for robust recommendation, which can provide personalized noise reduction for different users and items, and  ...  Robust recommendation aims at capturing true preference of users from noisy data, for which there are two lines of methods have been proposed.  ...  collaborative filtering.  ... 
arXiv:2106.15779v1 fatcat:3776g6w36rfjnnsmhpu3dgfuha

QoS Prediction for Neighbor Selection via Deep Transfer Collaborative Filtering in Video Streaming P2P Networks

Wenming Ma, Qian Zhang, Chunxiao Mu, Meng Zhang
2019 International Journal of Digital Multimedia Broadcasting  
Therefore, collaborative filtering (CF) methods could be used for QoS evaluation to select neighbors. However, we might use different QoS properties for different video streaming policies.  ...  The comprehensive experimental studies show that our approach offers higher prediction accuracy than the traditional collaborative filtering approaches.  ...  In this paper we proposed a novel neural style collaborative filtering method, DTCF (Deep Transfer Collaborative Filtering).  ... 
doi:10.1155/2019/1326831 fatcat:tipayxrpzrcqriavyvcnuiuwo4

Collaborative detection and filtering of shrew DDoS attacks using spectral analysis

Yu Chen, Kai Hwang
2006 Journal of Parallel and Distributed Computing  
Our defense method calls for collaborative detection and filtering (CDF) of shrew DDoS attacks.  ...  This novel scheme is suitable for either software or hardware implementation.  ...  Yu-Kwong Kwok of the University of Hong Kong for his earlier contributions to frequency-domain filtering techniques for cutting off shrew DDoS attacks.  ... 
doi:10.1016/j.jpdc.2006.04.007 fatcat:ytejlssmkvdghnbr4ofywea3ju

Convolutional Neural Network and Topic Modeling based Hybrid Recommender System

Hira Kanwal, Muhammad Assam, Abdul Jabbar, Salman Khan, Kalimullah
2020 International Journal of Advanced Computer Science and Applications  
Reviews that were written by users contain a large amount of information that can be utilized for more accurate predictions.  ...  To handle data sparsity problem most recommender systems utilized deep learning techniques for in-depth analysis of item content to generate more accurate recommendations.  ...  University of Engineering and information Technology (KFUEIT), College of Computer Science Zhejiang University, China and Military College of Signals National University of Science and Technology (NUST) for  ... 
doi:10.14569/ijacsa.2020.0110775 fatcat:scomusfihrfrzkyojygpl7ylqq

Light Graph Convolutional Collaborative Filtering with Multi-aspect Information

Denghua Mei, Niu Huang, Xin Li
2021 IEEE Access  
Graph Convolutional Network (GCN) has achieved great success and become a new state-of-the-art for collaborative filtering.  ...  In this paper, we propose a Light GCN based Aspect-level Collaborative Filtering model (LGC-ACF) to exploit multi-aspect user-item interaction information.  ...  in the embedding propagation process [8] , alleviate the data sparsity problem in collaborative filtering to some extent.  ... 
doi:10.1109/access.2021.3061915 fatcat:yeus775p5nebvcrpjbzqse7f7q

Anti-forensic Approach to Remove StegoContent from Images and Videos

P. P. Amritha, M. Sethumadhavan, R. Krishnan, Saibal Kumar Pal
2019 Journal of Cyber Security and Mobility  
For destroying stego content in transform domains, Gaussian and Median filters are preferred for textured images and while for non-textured images Wiener filter, median filter and Combination filters 1  ...  We can conclude that for textured images with different payloads embedded with spatial domain steganography, on average Gaussian and Median filter removes stego content above 80% while preserving quality  ... 
doi:10.13052/jcsm2245-1439.831 fatcat:tjclcuazszcajj24kdxyblgabi

Deep Variational Models for Collaborative Filtering-based Recommender Systems [article]

Jesús Bobadilla, Fernando Ortega, Abraham Gutiérrez, Ángel González-Prieto
2021 arXiv   pre-print
Deep learning provides accurate collaborative filtering models to improve recommender system results.  ...  On the other hand, data augmentation through variational autoencoder does not provide accurate results in the collaborative filtering field due to the high sparsity of recommender systems.  ... 2  ... 
arXiv:2107.12677v1 fatcat:eizxftcabzfirp66tgphrl7oai

Nonlocal transform-domain denoising of volumetric data with groupwise adaptive variance estimation

Matteo Maggioni, Alessandro Foi, Charles A. Bouman, Ilya Pollak, Patrick J. Wolfe
2012 Computational Imaging X  
BM4D implements the grouping and collaborative filtering paradigm, where similar cubes of voxels are stacked into a four-dimensional "group".  ...  Experiments on medical data corrupted by spatially varying Gaussian and Rician noise demonstrate the efficacy of the proposed approach in volumetric data denoising.  ...  The groupwise noise estimation embedded in the proposed BM4D-AV allows for a correct filtering of the noisy data in any section of the phantom.  ... 
doi:10.1117/12.912109 dblp:conf/cimaging/MaggioniF12 fatcat:jrlkzt7jgndxphz5iidlocdhsq

Neural Personalized Ranking via Poisson Factor Model for Item Recommendation

Yonghong Yu, Li Zhang, Can Wang, Rong Gao, Weibin Zhao, Jing Jiang
2019 Complexity  
In this paper, we propose a neural personalized ranking model for collaborative filtering with the implicit frequency feedback.  ...  Moreover, the traditional collaborating filtering methods generally use the linear functions to model the interactions between latent features.  ...  Collaborative Filtering. Collaborative filtering (CF) Complexity 3 Table 1 : 1 Statistics of Foursquare and Gowalla.  ... 
doi:10.1155/2019/3563674 fatcat:rc4kaow6fzg5dpppucsjdcewsy
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