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Collaborative Filtering with Attribution Alignment for Review-based Non-overlapped Cross Domain Recommendation [article]

Weiming Liu, Xiaolin Zheng, Mengling Hu, Chaochao Chen
2022 arXiv   pre-print
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]

Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, Qing He
2020 arXiv   pre-print
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]

Edemir Ferreira, Mário S. Alvim, Jefersson A. dos Santos
2018 arXiv   pre-print
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]

Lei Zhang, Xinbo Gao
2020 arXiv   pre-print
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

Peng Hao, Guangquan Zhang, Jie Lu
2016 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)  
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]

Abolfazl Farahani, Behrouz Pourshojae, Khaled Rasheed, Hamid R. Arabnia
2021 arXiv   pre-print
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

Yi Huang, Jiangtao Peng, Yujie Ning, Weiwei Sun, Qian Du
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
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]

Abolfazl Farahani, Sahar Voghoei, Khaled Rasheed, Hamid R. Arabnia
2020 arXiv   pre-print
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

Yu Zheng
2015 IEEE Transactions on Big Data  
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]

Tao He, Lianli Gao, Jingkuan Song, Yuan-Fang Li
2021 arXiv   pre-print
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]

Basura Fernando, Amaury Habrard, Marc Sebban, Tinne Tuytelaars
2014 arXiv   pre-print
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]

Ricardo Vilalta
2018 arXiv   pre-print
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

Wenwu Zhu, Xin Wang, Hongzhi Li
2019 IEEE transactions on circuits and systems for video technology (Print)  
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]

Bin Tong, Hao Shao, Bin-Hui Chou, Einoshin Suzuki
2010 Lecture Notes in Computer Science  
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

Qin Jiang, Yi Zhang, Kai Zheng
2022 Brain Sciences  
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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