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Collaborative Filtering with Attribution Alignment for Review-based Non-overlapped Cross Domain Recommendation
[article]
2022
arXiv
pre-print
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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. ...
To fill this gap, we propose Collaborative Filtering with Attribution Alignment model (CFAA), a cross-domain recommendation framework for the RNCDR problem. ...
Specifically, we term this problem as Review-based Non-overlapped Cross Domain Recommendation (RNCDR), where we aim to transfer knowledge from a relative dense source domain to a sparse target domain to ...
arXiv:2202.04920v1
fatcat:lqnltllc4ngvjmp47iqww3kg5i
A Comprehensive Survey on Transfer Learning
[article]
2020
arXiv
pre-print
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Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. ...
In this way, the dependence on a large number of target domain data can be reduced for constructing target learners. ...
There are some other studies about cross-domain recommendation [194] , [195] , [196] , [197] . ...
arXiv:1911.02685v3
fatcat:oeofarz7tnbtlblvta4evx3e34
A Comparative Study on Unsupervised Domain Adaptation Approaches for Coffee Crop Mapping
[article]
2018
arXiv
pre-print
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However, UDA methods may lead to negative transfer, which may indicate that domains are too different that transferring knowledge is not appropriate. ...
In this work, we investigate the application of existing unsupervised domain adaptation (UDA) approaches to the task of transferring knowledge between crop regions having different coffee patterns. ...
Distribution Analysis (JDA) [6] , Transfer Joint Matching (TJM) [7] , CORAL [10] , Joint Geometrical and Statistical Alignment (JGSA) [11] and transfer with no adaptation (NA). ...
arXiv:1806.02400v1
fatcat:hgnr5k7zeffalojml2ky5wz4ay
Transfer Adaptation Learning: A Decade Survey
[article]
2020
arXiv
pre-print
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TAL aims to build models that can perform tasks of target domain by learning knowledge from a semantic related but distribution different source domain. ...
A research problem is characterized as transfer adaptation learning (TAL) when it needs knowledge correspondence between different moments/domains. ...
ACKNOWLEDGMENT The author would like to thank the pioneer researchers in transfer learning, domain adaptation and other related fields. The author would also like to thank Dr. Mingsheng Long and Dr. ...
arXiv:1903.04687v2
fatcat:wurprqieffalnnp6isfkhh5y5i
Enhancing cross domain recommendation with domain dependent tags
2016
2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
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To handle this issue, social tags are utilized to bring disjoint domains together for knowledge transfer in cross-domain recommendation. ...
However, it is difficult to obtain a strong domain connection by exploiting a small amount of common tags, especially when the tagging data in target domain is too scarce to share enough common tags with ...
Acknowledgments This work is supported by the Australian Research Council (ARC) under discovery grant DP140101366 and the China Scholarship Council. ...
doi:10.1109/fuzz-ieee.2016.7737834
dblp:conf/fuzzIEEE/HaoZL16
fatcat:v6ac32ipb5a75eaast6xrvsgay
A Concise Review of Transfer Learning
[article]
2021
arXiv
pre-print
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Transfer learning aims to boost the performance of a target learner by applying another related source data. ...
situations where there is a discrepancy between domains and distributions. ...
Transfer learning aims to improve a target model's performance with insufficient or lack of annotated data by using the knowledge from another related source domain with adequate labeled data. ...
arXiv:2104.02144v1
fatcat:4a2boxyukredbprqzwidmj7t3u
Graph Embedding and Distribution Alignment for Domain Adaptation in Hyperspectral Image Classification
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Meanwhile, spatial and spectral features of HSI are used, and distribution alignment and subspace alignment are performed to minimize the spectral differences between domains. ...
Recent studies in cross-domain classification have shown that discriminant information of both source and target domains is very important. ...
By mining data correlation, it can realize the transfer of common knowledge between domains. ...
doi:10.1109/jstars.2021.3099805
fatcat:mda5ixj26bab5oxb4ykemo266y
A Brief Review of Domain Adaptation
[article]
2020
arXiv
pre-print
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Domain adaptation is a sub-field within machine learning that aims to cope with these types of problems by aligning the disparity between domains such that the trained model can be generalized into the ...
Besides, It presents some successful shallow and deep domain adaptation approaches that aim to deal with domain adaptation problems. ...
Subspace distribution alignment (SDA) [47] , extends the work in SA by aligning both subspace distributions and the bases at the same time. ...
arXiv:2010.03978v1
fatcat:kze4rweiurgxtlk4x6koigdqqq
Methodologies for Cross-Domain Data Fusion: An Overview
2015
IEEE Transactions on Big Data
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Traditional data mining usually deals with data from a single domain. In the big data era, we face a diversity of datasets from different sources in different domains. ...
These methods focus on knowledge fusion rather than schema mapping and data merging, significantly distinguishing between cross-domain data fusion and traditional data fusion studied in the database community ...
Transfer learning can even transfer knowledge between different learning tasks, e.g. from book recommendation to travel recommendation. ...
doi:10.1109/tbdata.2015.2465959
fatcat:flm37ozmhzcrfbrzeuagxm4l6a
Unsupervised Domain-adaptive Hash for Networks
[article]
2021
arXiv
pre-print
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However, it has not been applied to multiple-domain networks. In this work, we bridge this gap by developing an unsupervised domain-adaptive hash learning method for networks, dubbed UDAH. ...
intersected discriminators, and (4) semantic center alignment. ...
To address the first issue, we devise cross-domain intersected discriminators with a knowledge distillation loss. ...
arXiv:2108.09136v1
fatcat:duabx2ddrjdenn6j4llrmcrywi
Subspace Alignment For Domain Adaptation
[article]
2014
arXiv
pre-print
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Our method seeks a domain invariant feature space by learning a mapping function which aligns the source subspace with the target one. ...
In this paper, we introduce a new domain adaptation (DA) algorithm where the source and target domains are represented by subspaces spanned by eigenvectors. ...
[24] ), to the best of our knowledge, it has never been used to create linear subspaces in conjunction with PCA to improve subspace-based domain adaptation methods. ...
arXiv:1409.5241v2
fatcat:23uguyb7are5houuvqwv62xhsm
Transfer Learning in Astronomy: A New Machine-Learning Paradigm
[article]
2018
arXiv
pre-print
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Transfer learning provides a robust and practical solution to leverage information from one domain to improve the accuracy of a model built on a different domain. ...
The new emerging area is referred to as transfer learning. ...
It then attempts to align the projected source dataset with the projected target dataset in this common subspace using a subspace alignment matrix. ...
arXiv:1812.10403v1
fatcat:b4zmxaqxlncetenfrberdwvuwi
Multi-modal Deep Analysis for Multimedia
2019
IEEE transactions on circuits and systems for video technology (Print)
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2019 pre-print arXiv:1910.04964v1 fatcat:76yma7ca4fhajduyuyuos3csziOther Versions
fusion: multi-modal fusion of data with domain knowledge. ...
On knowledge-guided fusion, we discuss the approaches for fusing knowledge with data and four exemplar applications that require various kinds of domain knowledge, including multi-modal visual question ...
, video summarization, visual pattern mining and recommendation, which need diverse domain knowledge for multi-modal fusion of data with knowledge. ...
doi:10.1109/tcsvt.2019.2940647
fatcat:l4tchrkgrnaeradvc4nhfan2w4
Semi-supervised Projection Clustering with Transferred Centroid Regularization
[chapter]
2010
Lecture Notes in Computer Science
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We also invent a transferred centroid regularization, which acts as a bridge to transfer the constraint knowledge to the target domain, to formulate this geometric structure formed by the centroids from ...
One major challenging issue is how to exploit constraint knowledge from multiple source domains and transfer it to the target domain where all the data are unlabeled. ...
Bin Tong and Hao Shao are sponsored by the China Scholarship Council (CSC). ...
doi:10.1007/978-3-642-15939-8_20
fatcat:t7s5qyevx5dthbbg7gvihosyfq
Motor Imagery Classification via Kernel-Based Domain Adaptation on an SPD Manifold
2022
Brain Sciences
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In KMDA, the covariance matrices are aligned in the Riemannian manifold, and then are mapped to a high dimensional space by a log-Euclidean metric Gaussian kernel, where subspace learning is performed ...
Additionally, the overall accuracy was further improved by 2.86% with the E-frames. ...
Similarly, the manifold embedded knowledge transfer (MEKT) framework [33] first whitened the SPD matrices of cross-subjects to an identity matrix, and then performed domain adaptation using tangent vectors ...
doi:10.3390/brainsci12050659
fatcat:uwrzer2fuzdjtebiijjsam4jbu
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