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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 data in related recommenders are abundant; (ii) data domains that are associated with multiple sources of heterogeneous data, and represent a scenario where user data in source domains (e.g., binary ...
doi:10.1007/978-1-4899-7637-6_27
fatcat:4kregbpxajbnxmg6xywfmm22ki
Tutorial on cross-domain recommender systems
2014
Proceedings of the 8th ACM Conference on Recommender systems - RecSys '14
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Cross-domain recommender systems aim to generate or enhance personalized recommendations in a target domain by exploiting knowledge (mainly user preferences) form other source domains. ...
This may beneficial for generating better recommendations, e.g. mitigating the cold-start and sparsity problems in a target domain, and enabling personalized cross-selling for items from multiple domains ...
A common practice with cross-domain recommender systems is to evaluate their relevance through prediction metrics such as MAE and RMSE. ...
doi:10.1145/2645710.2645777
dblp:conf/recsys/CantadorC14
fatcat:uut6h7xanzal7h4mgz65w3ocqi
Cross-domain Recommendation with Probabilistic Knowledge Transfer
[chapter]
2018
Lecture Notes in Computer Science
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In this paper, we propose a cross-domain recommendation method with probabilistic knowledge transfer. ...
To alleviate the data sparsity problem, cross-domain recommendation methods are developed to share group-level knowledge in several domains so that recommendation in the domain with scarce data can benefit ...
By taking the advantages of data in multiple domains, cross-domain recommender systems can exploit the relatively dense data in the source domain to assist recommendation with scarce data in the target ...
doi:10.1007/978-3-030-04182-3_19
fatcat:toghqpai6zgarn63csa7iqsj4m
Cross-Domain Collaborative Filtering via Translation-based Learning
[article]
2019
arXiv
pre-print
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The main challenge of cross-domain recommendation is to weigh and learn users' different behaviors in multiple domains. ...
In our model, we learn the embedding space with translation vectors and capture high-order feature interactions in users' multiple preferences across domains. ...
Cross-Domain collaborative filtering with FMs, presented in [4] , is a stateof-the-art cross-domain recommendation. ...
arXiv:1908.06169v1
fatcat:xvblxtn4cjalzafcuk6e67jo2a
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 ...
Cross domain recommender systems have been increasingly valuable for helping consumers identify the most satisfying items from different categories. ...
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
Latent User Linking for Collaborative Cross Domain Recommendation
[article]
2019
arXiv
pre-print
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As a result, we propose a Variational Autoencoder based network model for cross-domain linking with added contextualization to handle sparse data and for better transfer of cross-domain knowledge. ...
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 ...
In the case of cross-domain recommendation, CONET has extended the MLP model to multiple domains [9] . ...
arXiv:1908.06583v1
fatcat:curd5j6arfasfmzfzk5fbsfw4u
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. ...
Datasets with Multiple Domains Datasets that contain information from multiple domains are the most widely used datasets in cross-domain recommendation as they perfectly fit the cross-domain recommendation ...
arXiv:2108.03357v1
fatcat:sitcklnxibafjomlq77rqvboia
TALMUD
2012
Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12
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Experiments with several datasets reveal that, using multiple sources and the relatedness between domains improves accuracy of results. ...
Cross-domain recommenders address the sparsity problem by using Machine Learning (ML) techniques to transfer knowledge from a dense domain into a sparse target domain. ...
Recommender Systems Cross-Domain Techniques In recommender systems, the transfer learning problem is often known as cross-domain recommendation [13] . ...
doi:10.1145/2396761.2396817
dblp:conf/cikm/MorenoSRS12
fatcat:hbpnbturlfdh7ovmsmgqmukwsq
Transfer collaborative filtering from multiple sources via consensus regularization
2018
Neural Networks
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The TRACER framework handles the information inconsistency with a consensus regularization, which enforces the outputs from multiple sources to converge. ...
Rich information is available in many source domains, which can better complement the data in the target domain than that from a single source. ...
In order to integrate more information from different domains for better recommendation, cross-domain recommendation considers combining data from different domains with the original target data (Fernández-Tobías ...
doi:10.1016/j.neunet.2018.08.022
pmid:30243052
fatcat:vx5wkkzwarbffc2mcmm2ousblu
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. ...
Extensive experiments on 24 Amazon rating datasets show the effectiveness of JSCN in the cross-domain recommendation, with 9.2% improvement on recall and 36.4% improvement on MAP compared with state-of-the-art ...
Comparison with Different Source Domains In this section, we report the cross-domain recommendation results of JSCN-β on the target domain with different source domains w.r.t. MAP@20 in Fig. 7 . ...
arXiv:1910.08219v1
fatcat:3wyulfy6ffexlo6zac3o2gbetm
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. ...
On the other hand, to overcome the potential inconsistency problem between different domains, we equip the neighbor model with a selective learning mechanism so that domain-independent items gain more ...
Cross domain methods with user/item aligned. [3] studies some earliest CDCF models including the cross domain neighbor model. ...
doi:10.1007/978-3-319-63558-3_27
fatcat:kzlfi5iz5fc57hmri44z6d22ci
Graph Factorization Machines for Cross-Domain Recommendation
[article]
2020
arXiv
pre-print
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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 ...
Meanwhile, cross-domain recommendation has emerged as a viable method to solve the data sparsity problem in recommender systems. ...
Cross-Domain Recommendation Cross-domain recommendation can take the advantage of existing large scale data in the source domain and improve the data sparsity and recommendation quality in the related ...
arXiv:2007.05911v1
fatcat:f6xugvw5ifglzeprw542gad72u
Semantic clustering-based cross-domain recommendation
2014
2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)
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Cross domain recommendation systems exploit tags, textual descriptions or ratings available for items in one domain to recommend items in multiple domains. ...
In this paper, we introduce the concept of a common semantic space, spanning multiple domains, using topic modeling of semantic clustered vocabularies of distinct domains. ...
Algorithm We will now describe the SCD algorithm in detail for producing cross domain recommendations from a source domain S to a target domain T . ...
doi:10.1109/cidm.2014.7008659
dblp:conf/cidm/KumarKHCA14
fatcat:nnfjh27tvrfuzi3nwb4gposwka
An open framework for multi-source, cross-domain personalisation with semantic interest graphs
2012
Proceedings of the sixth ACM conference on Recommender systems - RecSys '12
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I propose an open framework as an alternative, which enables cross-domain recommendations with domain-agnostic user profiles modelled as semantic interest graphs. ...
Cross-domain recommendations are currently available in closed, proprietary social networking ecosystems such as Facebook, Twitter and Google+. ...
In order to provide users with the benefit of multi-source, cross-domain recommendations, users currently need to accept a trade-off [3] regarding their privacy, trust, data ownership and control : Privacy ...
doi:10.1145/2365952.2366030
dblp:conf/recsys/Heitmann12
fatcat:vzur73nairc63ojm53pef7m2xa
Privacy-Preserving Matrix Factorization for Cross-Domain Recommendation
2021
IEEE Access
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PROBLEM DEFINITION Cross-domain recommender systems leverage sufficient ratings in the source domain to generate better and accurate recommendations in the target domain. ...
The goal of this study is dedicated to generating privacypreserving recommendations in a cross-domain scenario for a target domain with a sparse dataset. ...
doi:10.1109/access.2021.3091426
fatcat:awvjql4tl5ce5olqc7xssdi44u
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