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Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text [article]

Guangneng Hu, Yu Zhang, Qiang Yang
2019 arXiv   pre-print
We propose a novel neural model to smoothly enable Transfer Meeting Hybrid (TMH) methods for cross-domain recommendation with unstructured text in an end-to-end manner.  ...  TMH attentively extracts useful content from unstructured text via a memory module and selectively transfers knowledge from a source domain via a transfer network.  ...  It is the first deep model that transfers cross-domain knowledge for recommendation with unstructured text using attention based neural networks. • We interpret the memory networks to attentively exploit  ... 
arXiv:1901.07199v1 fatcat:ti7l7rv2vzca7cauwh4iidaceq

A Parallel Deep Neural Network Using Reviews and Item Metadata for Cross-domain Recommendation

Wenxing Hong, Nannan Zheng, Ziang Xiong, Zhiqiang Hu
2020 IEEE Access  
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 Cross-domain recommender systems aim to make recommendations in a target domain by exploiting knowledge from other source domains [13] .  ... 
doi:10.1109/access.2020.2977123 fatcat:4lfqtfpt4jdplf2j66do2p325y

RDF-to-Text Generation with Graph-augmented Structural Neural Encoders

Hanning Gao, Lingfei Wu, Po Hu, Fangli Xu
2020 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  
To address these issues, we propose to jointly learn local and global structure information via combining two new graph-augmented structural neural encoders (i.e., a bidirectional graph encoder and a bidirectional  ...  Then, we define the Dual-Target Cross-Domain Recommendation as follows. Dual-Target Cross-Domain Recommendation (DTCDR).  ...  Then, we propose a novel Graphical and Attentional framework for Dual-Target Cross-Domain Recommendation, called GA-DTCDR. Finally, we present the detailed components of GA-DTCDR.  ... 
doi:10.24963/ijcai.2020/415 dblp:conf/ijcai/ZhuWCLZ20 fatcat:bfw4nsudpjbvlnbpjdyssk3mla

Information Fusion-Based Deep Neural Attentive Matrix Factorization Recommendation

Zhen Tian, Lamei Pan, Pu Yin, Rui Wang
2021 Algorithms  
In this paper, we propose the information fusion-based deep neural attentive matrix factorization (IFDNAMF) recommendation model, which introduces the attribute information and adopts the element-wise  ...  product between the different information domains to learn the cross-features when conducting information fusion.  ...  Figure 1 . 1 Information fusion-based deep neural attentive matrix factorization recommendation model structure.  ... 
doi:10.3390/a14100281 fatcat:sujap4c2qzb57pc75jl3fanpvm

Graph Factorization Machines for Cross-Domain Recommendation [article]

Dongbo Xi, Fuzhen Zhuang, Yongchun Zhu, Pengpeng Zhao, Xiangliang Zhang, Qing He
2020 arXiv   pre-print
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  ...  Recently, graph neural networks (GNNs) have been successfully applied to recommender systems.  ...  Some works [14] , [24] , [25] extended the classical Collaborative Filtering (CF) to the cross-domain scenario. Recently, neural networks have been used to implement cross-domain recommendation.  ... 
arXiv:2007.05911v1 fatcat:f6xugvw5ifglzeprw542gad72u

2020 Index IEEE Transactions on Artificial Intelligence Vol. 1

2020 IEEE Transactions on Artificial Intelligence  
., +, TAI Aug. 2020 62-73 Self-Supervised Pose Adaptation for Cross-Domain Image Animation.  ...  ., +, TAI Oct. 2020 167-180 Self-Supervised Pose Adaptation for Cross-Domain Image Animation.  ... 
doi:10.1109/tai.2021.3089904 fatcat:53o6433ljne3lblvr5fuy66lou

A Unified Framework for Cross-Domain and Cross-System Recommendations [article]

Feng Zhu, Yan Wang, Jun Zhou, Chaochao Chen, Longfei Li, Guanfeng Liu
2021 arXiv   pre-print
Cross-Domain Recommendation (CDR) and Cross-System Recommendation (CSR) have been proposed to improve the recommendation accuracy in a target dataset (domain/system) with the help of a source one with  ...  To do this, we propose a unified framework, called GA (based on Graph embedding and Attention techniques), for all three scenarios.  ...  INTRODUCTION Background T ARGETING data sparsity problem, Cross-Domain Recommendation (CDR) [1] and Cross-System Recommendation (CSR) [2] , [3] have been proposed to leverage the richer information  ... 
arXiv:2108.07976v1 fatcat:gfie4f5b4ncuvotz7wiqlhaice

A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions [article]

Tianzi Zang, Yanmin Zhu, Haobing Liu, Ruohan Zhang, Jiadi Yu
2021 arXiv   pre-print
Over the last decade, many efforts have been engaged for cross-domain recommendation. Recently, with the development of deep learning and neural networks, a large number of methods have emerged.  ...  In this survey paper, we first proposed a two-level taxonomy of cross-domain recommendation which classifies different recommendation scenarios and recommendation tasks.  ...  [33] designed a model named Neural Attentive-Transfer Recommendation (NATR) which focused on sharing item embeddings between domains.  ... 
arXiv:2108.03357v1 fatcat:sitcklnxibafjomlq77rqvboia

RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendation [article]

Cheng Hsu, Cheng-Te Li
2021 arXiv   pre-print
We propose a novel deep learning-based model, Relational Temporal Attentive Graph Neural Networks (RetaGNN), for holistic SR. The main idea of RetaGNN is three-fold.  ...  Sequential recommendation (SR) is to accurately recommend a list of items for a user based on her current accessed ones.  ...  In this paper, we propose a novel deep learning-based model, RElational Temporal Attentive Graph Neural Network (RetaGNN), for sequential recommendation.  ... 
arXiv:2101.12457v1 fatcat:i4pdzfjtifeq3g4mxz3b2e3se4

MiNet: Mixed Interest Network for Cross-Domain Click-Through Rate Prediction [article]

Wentao Ouyang, Xiuwu Zhang, Lei Zhao, Jinmei Luo, Yu Zhang, Heng Zou, Zhaojie Liu, Yanlong Du
2020 arXiv   pre-print
Nevertheless, ads are usually displayed with natural content, which offers an opportunity for cross-domain CTR prediction.  ...  MiNet contains two levels of attentions, where the item-level attention can adaptively distill useful information from clicked news / ads and the interest-level attention can adaptively fuse different  ...  [8] propose the Neural Attentive Transfer Recommendation (NATR) for cross-domain recommendation without sharing userrelevant data. Hu et al.  ... 
arXiv:2008.02974v1 fatcat:lhxuowldd5apvhq5wlojgkfbkq

Artificial intelligence in recommender systems

Qian Zhang, Jie Lu, Yaochu Jin
2020 Complex & Intelligent Systems  
learning, genetic algorithms, evolutionary algorithms, neural networks and deep learning, and active learning.  ...  recommender systems.  ...  The recent developments of deep neural networks are also applied in knowledge transfer and cross-domain recommendation.  ... 
doi:10.1007/s40747-020-00212-w fatcat:ev3cyoy2mjeuhmq3rymkx2shsy

CoNet: Collaborative Cross Networks for Cross-Domain Recommendation [article]

Guangneng Hu, Yu Zhang, Qiang Yang
2018 arXiv   pre-print
In this paper, we propose a novel transfer learning approach for cross-domain recommendation by using neural networks as the base model.  ...  The cross-domain recommendation technique is an effective way of alleviating the data sparse issue in recommender systems by leveraging the knowledge from relevant domains.  ...  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

Dual Metric Learning for Effective and Efficient Cross-Domain Recommendations [article]

Pan Li, Alexander Tuzhilin
2021 arXiv   pre-print
cross-domain recommendation performance.  ...  Cross domain recommender systems have been increasingly valuable for helping consumers identify useful items in different applications.  ...  Neural Attention [70] and Adversarial Network [71] to learn user preferences and produce final recommendations.  ... 
arXiv:2104.08490v2 fatcat:v4tdolu45je3dohehifljaq3yq

A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation [article]

Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, Meng Wang
2021 arXiv   pre-print
neural networks.  ...  Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks.  ...  domain with attention based transfer learning models [157] .  ... 
arXiv:2104.13030v3 fatcat:7bzwaxcarrgbhe36teik2rhl6e

TrNews: Heterogeneous User-Interest Transfer Learning for News Recommendation [article]

Guangneng Hu, Qiang Yang
2021 arXiv   pre-print
We investigate how to solve the cross-corpus news recommendation for unseen users in the future. This is a problem where traditional content-based recommendation techniques often fail.  ...  To take advantage of the existing corpus, we propose a transfer learning model (dubbed as TrNews) for news recommendation to transfer the knowledge from a source corpus to a target corpus.  ...  Conclusion We investigate the cross-domain news recommendation via transfer learning.  ... 
arXiv:2101.05611v2 fatcat:t3aj3eakfrgnpjd3t5wurefd2a
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