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DDTCDR: Deep Dual Transfer Cross Domain Recommendation
[article]
2019
arXiv
pre-print
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Combining with autoencoder approach to extract the latent essence of feature information, we propose Deep Dual Transfer Cross Domain Recommendation (DDTCDR) model to provide recommendations in respective ...
To address these concerns, in this paper we propose a novel approach to cross-domain recommendations based on the mechanism of dual learning that transfers information between two related domains in an ...
Cross Domain and Transfer Learning-based Recommendations Cross domain recommendation approach [3] constitutes a powerful tool to deal with the data sparsity problem. ...
arXiv:1910.05189v1
fatcat:y5mqqv3gebgqbgakxk4qzubgmq
A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions
[article]
2021
arXiv
pre-print
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Traditional recommendation systems are faced with two long-standing obstacles, namely, data sparsity and cold-start problems, which promote the emergence and development of Cross-Domain Recommendation ...
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. ...
They proposed a cross-domain recommendation approach with semantic correlations in tagging systems (SCT). ...
arXiv:2108.03357v1
fatcat:sitcklnxibafjomlq77rqvboia
Cross-Domain Recommender Systems
[chapter]
2015
Recommender Systems Handbook
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Cross-domain recommender systems, thus, aim to generate or enhance recommendations in a target domain by exploiting knowledge from source domains. ...
and sparsity problems in a target domain, or enabling personalized crossselling recommendations for items from multiple domains. ...
The third deals with combining (or mediating) information from several single-domain recommender systems [6] . ...
doi:10.1007/978-1-4899-7637-6_27
fatcat:4kregbpxajbnxmg6xywfmm22ki
Cross Domain Recommendation Using Semantic Similarity and Tensor Decomposition
2016
Procedia Computer Science
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In this information age Recommender system is a very useful tool, because it has the capability of filtering the information according to user interest and provide personalized suggestion. ...
One of the major drawbacks of the classical recommender system is that, they deal with the only single domain.In real world scenario domains could be related to each other by some common information. ...
Cross domain recommender systems based on Code book transfer (CBT) tried to use non overlapping information [6] . CBT has limitation about data in both domains should be of the same size. ...
doi:10.1016/j.procs.2016.05.239
fatcat:weo3ib2ei5d6ljfe3sc7tcyhmy
Dual Metric Learning for Effective and Efficient Cross-Domain Recommendations
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2104.08490v1 fatcat:lo2ydp7ltzdvfoudkmacftludm
To address these issues, in this paper we propose a novel cross-domain recommendation model based on dual learning that transfers information between two related domains in an iterative manner until the ...
Cross domain recommender systems have been increasingly valuable for helping consumers identify useful items in different applications. ...
Cross Domain Recommendations Cross-domain recommendation approach [2] constitutes a powerful tool to deal with the data sparsity problem. ...
arXiv:2104.08490v2
fatcat:v4tdolu45je3dohehifljaq3yq
Joint User Modeling Across Aligned Heterogeneous Sites Using Neural Networks
[chapter]
2017
Lecture Notes in Computer Science
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The quality of user modeling is crucial for personalized recommender systems. ...
To alleviate such problem, researchers propose cross-domain models to leverage user actions from other domains within same site. ...
Cross-Domain Recommendation Following the intuition that user's preferences is consistent across domains, researchers propose cross-domain recommendation to leverage user actions from other domains as ...
doi:10.1007/978-3-319-71249-9_48
fatcat:dxfmmoo6jvd5dbixhtov4k3x2a
A Multifaceted Model for Cross Domain Recommendation Systems
[chapter]
2017
Lecture Notes in Computer Science
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In this paper, we introduce a Multifaceted Cross-Domain Recommendation System (MCDRS) which incorporates two different types of collaborative filtering for cross domain RSs. ...
Several cross-domain RSs have been proposed in the past decade in order to reduce the sparsity issues via transferring knowledge. ...
Introduction With the boosting of online services, recommendation systems (RS) are playing an increasingly important role in filtering information for customers. ...
doi:10.1007/978-3-319-63558-3_27
fatcat:kzlfi5iz5fc57hmri44z6d22ci
Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text
[article]
2019
arXiv
pre-print
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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. ...
Another thread is to transfer knowledge from other source domains such as improving the movie recommendation with the knowledge from the book domain, leading to transfer learning methods. ...
It is a naive knowledge
transfer approach applied for cross-domain recommendation. ...
arXiv:1901.07199v1
fatcat:ti7l7rv2vzca7cauwh4iidaceq
A unified framework of active transfer learning for cross-system recommendation
2017
Artificial Intelligence
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Recommender systems, especially the newly launched ones, have to deal with the data-sparsity issue, where little existing rating information is available. ...
In this paper, we propose a framework to construct entity correspondence with limited budget by using active learning to facilitate knowledge transfer across recommender systems. ...
This verifies the conclusion that making use of cross-system entitycorrespondences as a bridge is useful for knowledge transfer across recommender systems. ...
doi:10.1016/j.artint.2016.12.004
fatcat:nnprviag4ve3roji4woyjku7wm
Mixed Information Flow for Cross-domain Sequential Recommendations
[article]
2020
arXiv
pre-print
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2020 pre-print arXiv:2012.00485v2 fatcat:vrx3pp4xzrgibeb624nbbl2m3uOther Versions
One of the key challenges in cross-domain sequential recommendation is to grasp and transfer the flow of information from multiple domains so as to promote recommendations in all domains. ...
In this paper, we propose a mixed information flow network for cross-domain sequential recommendation to consider both the flow of behavioral information and the flow of knowledge by incorporating a behavior ...
DCDIR: A deep cross-domain recommendation system for cold
start users in insurance domain. ...
arXiv:2012.00485v3
fatcat:kl4klnly75aodjudrprio6i4cm
A Parallel Deep Neural Network Using Reviews and Item Metadata for Cross-domain Recommendation
2020
IEEE Access
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Cross-domain recommendation is an effective technique to alleviate the data sparsity problem in recommender systems by utilizing the information from relevant domains. ...
CD-DNN builds a single mapping for user features in the latent space, so that the network for user is optimized together with item features from other domains. ...
The above two effective solutions inspired us to combine content information and cross-domain information into a novel cross-domain recommendation system. ...
doi:10.1109/access.2020.2977123
fatcat:4lfqtfpt4jdplf2j66do2p325y
Cross-domain Recommendation by Combining Feature Tags with Transfer Learning
2015
International Journal of u- and e- Service, Science and Technology
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Most recommender systems based on collaborative filtering aim to provide recommendations for a user in one domain. But data sparsity is a major problem for collaborative filtering techniques. ...
In this paper, we propose a transfer model which learning the common feature tags from other domain. ...
CF in single domain: In the field of single recommendation system, Collaborative filtering recommendation is a popular technology in information filtering and information system. ...
doi:10.14257/ijunesst.2015.8.10.06
fatcat:dyvokgazyrcrxigyf6nd67prcu
Cross-Domain Recommendation Based on Sentiment Analysis and Latent Feature Mapping
2020
Entropy
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Cross-domain recommendation is a promising solution in recommendation systems by using relatively rich information from the source domain to improve the recommendation accuracy of the target domain. ...
Finally, this paper proves the effectiveness of the proposed CDR-SAFM framework by comparing it with existing recommendation algorithms in a cross-domain scenario on the Amazon dataset. ...
transfer in cross-domain recommendation. ...
doi:10.3390/e22040473
pmid:33286247
fatcat:2ioea6b5p5c4dc64ljdybiuuzi
Latent User Linking for Collaborative Cross Domain Recommendation
[article]
2019
arXiv
pre-print
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With the widespread adoption of information systems, recommender systems are widely used for better user experience. Collaborative filtering is a popular approach in implementing recommender systems. ...
In this publication, we propose a deep learning method for cross-domain recommender systems through the linking of cross-domain user latent representations as a form of knowledge transfer across domains ...
INTRODUCTION With the widespread adoption of information systems, recommender systems are widely used in many information systems such as e-commerce sites, social media networks and online news portals ...
arXiv:1908.06583v1
fatcat:curd5j6arfasfmzfzk5fbsfw4u
JSCN: Joint Spectral Convolutional Network for Cross Domain Recommendation
[article]
2019
arXiv
pre-print
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Cross-domain recommendation can alleviate the data sparsity problem in recommender systems. ...
As a result, the high-order comprehensive connectivity information can be extracted by the spectral convolutions and the information can be transferred across domains with the domain-invariant user mapping ...
To remedy the data sparsity issue, broad-leraning based model [7] and cross-domain recommender system [4] , [8] are proposed where the information from other source domains can be transferred to the ...
arXiv:1910.08219v1
fatcat:3wyulfy6ffexlo6zac3o2gbetm
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