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CoNet: Collaborative Cross Networks for Cross-Domain Recommendation
[article]
2018
arXiv
pre-print
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We assume that hidden layers in two base networks are connected by cross mappings, leading to the collaborative cross networks (CoNet). ...
In this paper, we propose a novel transfer learning approach for cross-domain recommendation by using neural networks as the base model. ...
Conclusions We proposed a novel approach to perform knowledge transfer learning for cross-domain recommendation via collaborative cross networks (CoNet). ...
arXiv:1804.06769v2
fatcat:g5t3u3vxjbahbj7gpx2mwteh54
Physiological-signal-based mental workload estimation via transfer dynamical autoencoders in a deep learning framework
2019
Neurocomputing
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We also investigated how to select TDAE hyper-parameters and found its superiority in accuracy can be achieved with proper filter orders. ...
The TDAE consists of three consecutively-connected modules, which are termed as feature filter, abstraction filter, and transferred MW classifier. ...
Transfer learning emphasizes sharing knowledge from a source domain to a target domain [17] . ...
doi:10.1016/j.neucom.2019.02.061
fatcat:cdrrds5du5ah5h57d2baha6hdi
Graph Factorization Machines for Cross-Domain Recommendation
[article]
2020
arXiv
pre-print
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However, most existing cross-domain recommendation methods might fail when confronting the graph-structured data. ...
Besides, based on general cross-domain recommendation experiments, we also demonstrate that our cross-domain framework could not only contribute to the cross-domain recommendation task with the GFM, but ...
However, it is hard for the traditional collaborative filtering methods to utilize the graph-structured data. ...
arXiv:2007.05911v1
fatcat:f6xugvw5ifglzeprw542gad72u
Collaborative Filtering with Attribution Alignment for Review-based Non-overlapped Cross Domain Recommendation
[article]
2022
arXiv
pre-print
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To fill this gap, we propose Collaborative Filtering with Attribution Alignment model (CFAA), a cross-domain recommendation framework for the RNCDR problem. ...
Cross-Domain Recommendation (CDR) has been popularly studied to utilize different domain knowledge to solve the data sparsity and cold-start problem in recommender systems. ...
for Cross-Domain Recommendation via Transferring Rating Patterns (DARec) adopts adversarial training strategy to extract and transfer knowledge patterns for shared users across domains. ...
arXiv:2202.04920v1
fatcat:lqnltllc4ngvjmp47iqww3kg5i
Model recommendation: Generating object detectors from few samples
2015
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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Army Research Laboratory (ARL) under the Collaborative Technology Alliance Program, Cooperative Agreement W911NF-10-2-0016, and by an AWS in Education Coursework Grant. ...
However, models informative across categories and datasets could be achieved via unsupervised hyper-training. ...
Collaborative Filtering Based on the probe set ratings and the ratings store, collaborative filtering techniques predict the ratings of the entire library. ...
doi:10.1109/cvpr.2015.7298770
dblp:conf/cvpr/WangH15
fatcat:auejhxzbibb7zb5o3fmjfay2de
2020 Index IEEE Transactions on Image Processing Vol. 29
2020
IEEE Transactions on Image Processing
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.,
+, TIP 2020 8043-8054
Cross-Modality Person Re-Identification via Modality-Aware Collaborative
Ensemble Learning. ...
Meng, M., +, TIP 2020 186-198 Cross-Modality Person Re-Identification via Modality-Aware Collaborative Ensemble Learning. ...
doi:10.1109/tip.2020.3046056
fatcat:24m6k2elprf2nfmucbjzhvzk3m
Cross-Subject EEG Feature Selection for Emotion Recognition Using Transfer Recursive Feature Elimination
2017
Frontiers in Neurorobotics
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The effectiveness of the T-RFE algorithm for such cross-subject emotion classification paradigm has been validated by DEAP database. ...
A validating set is introduced to independently determine the optimal hyper-parameter and the feature ranking of the T-RFE model aiming at controlling the overfitting. ...
The linear model has a parsimonious structure than the non-linear model and the SVM follows the principle of the structural risk minimization. ...
doi:10.3389/fnbot.2017.00019
pmid:28443015
pmcid:PMC5385370
fatcat:p274i35vpfbrlga4pfszdhs5qy
Towards Equivalent Transformation of User Preferences in Cross Domain Recommendation
[article]
2022
arXiv
pre-print
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Cross domain recommendation (CDR) is one popular research topic in recommender systems. ...
The majority of recent methods have explored the shared-user representation to transfer knowledge across domains. ...
CCCFNet [51] combines collaborative filtering and content-based filtering into one unified matrix factorization framework. ...
arXiv:2009.06884v2
fatcat:aeyzv4kotbakrirf4mdhqt6luy
Learning a meta-level prior for feature relevance from multiple related tasks
2007
Proceedings of the 24th international conference on Machine learning - ICML '07
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We show that transfer learning of feature relevance improves performance on two real data sets which illustrate such settings: (1) predicting ratings in a collaborative filtering task, and (2) distinguishing ...
Our approach transfers the meta-priors among different tasks, allowing it to deal with settings where tasks have non-overlapping features or where feature relevance varies over the tasks. ...
Traditionally, a prior for feature relevance is selected by hand, or via cross-validation. ...
doi:10.1145/1273496.1273558
dblp:conf/icml/LeeCVK07
fatcat:peh5k4pmdrhsrdmqd7s54cfgdu
A Parallel Deep Neural Network Using Reviews and Item Metadata for Cross-domain Recommendation
2020
IEEE Access
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INDEX TERMS Cross-domain recommendation, convolutional neural networks, rating prediction. ...
In this paper, we propose Crossdomain Deep Neural Network (CD-DNN) for the cross-domain recommendation. ...
Cross-domain recommendation is a technology for knowledge transfer between multiple domains. ...
doi:10.1109/access.2020.2977123
fatcat:4lfqtfpt4jdplf2j66do2p325y
Bilinear noise subtraction at the GEO 600 observatory
[article]
2020
arXiv
pre-print
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The time-domain filtering efficiency is observed to depend upon the system identification process especially when the involved transfer functions cover a large dynamic range and have multiple resonant ...
The filter coefficients are updated periodically to account for any non-stationarities that can arise within the coupling. ...
Updating the filter coefficients to tackle the non-stationaries has recently been shown to provide better subtraction for the case of non-linear noise observed in the LIGO detectors [38] . ...
arXiv:2001.00242v1
fatcat:tcfeaoupubglfk7abumzhczrvu
A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions
[article]
2021
arXiv
pre-print
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We then introduce and summarize existing cross-domain recommendation approaches under different recommendation scenarios in a structured manner. We also organize datasets commonly used. ...
In this survey paper, we first proposed a two-level taxonomy of cross-domain recommendation which classifies different recommendation scenarios and recommendation tasks. ...
They proposed a new knowledge transfer technique, called the hyper-structure transfer (HST) [61] , that captured the non-linear correlations of knowledge between domains. ...
arXiv:2108.03357v1
fatcat:sitcklnxibafjomlq77rqvboia
2021 Index IEEE Transactions on Image Processing Vol. 30
2021
IEEE Transactions on Image Processing
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Sun, Y., +, TIP 2021 6277-6291 Handheld computers Spatial-Spectral Structured Sparse Low-Rank Representation for Hyper-TIP 2021 1332-1341 DotFAN: A Domain-Transferred Face Augmentation Net. ...
Imaging via Non-Iterative Subspace-Based Fusion. ...
doi:10.1109/tip.2022.3142569
fatcat:z26yhwuecbgrnb2czhwjlf73qu
A Parallel Multiscale Filter Bank Convolutional Neural Networks for Motor Imagery EEG Classification
2019
Frontiers in Neuroscience
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In this study, we propose a parallel multiscale filter bank convolutional neural network (MSFBCNN) for MI classification. ...
We introduce a layered end-to-end network structure, in which a feature-extraction network is used to extract temporal and spatial features. ...
Additionally, square and log non-linear operations enhance the non-linear expression ability of the feature reduction layer. ...
doi:10.3389/fnins.2019.01275
pmid:31849587
pmcid:PMC6901997
fatcat:yot5qoufcbgh3mahxiswvvccc4
Deep Learning and Domain Transfer for Orca Vocalization Detection
2020
2020 International Joint Conference on Neural Networks (IJCNN)
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In this paper, we study the difficulties of domain transfer when training deep learning models, on a specific task that is orca vocalization detection. ...
We thus explore approaches to compensate on the difficulties faced with domain transfer, with two convolutionnal neural networks (CNN) architectures, one that works in the time-frequency domain, and one ...
We thank first the OrcaLab direction Paul Spong and Helena Symonds and collaborators for their incredible inspired work. ...
doi:10.1109/ijcnn48605.2020.9207567
dblp:conf/ijcnn/BestFPPMSSG20
fatcat:m5bk5pdeczhnbb7niu6toojqd4
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