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CoNet: Collaborative Cross Networks for Cross-Domain Recommendation
[article]
2018
arXiv
pre-print
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In this paper, we propose a novel transfer learning approach for cross-domain recommendation by using neural networks as the base model. ...
We assume that hidden layers in two base networks are connected by cross mappings, leading to the collaborative cross networks (CoNet). ...
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
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 ...
CONET [9]: Collaborative cross networks is the most
recent state of the art cross-domain recommendation
method. ...
arXiv:1908.06583v1
fatcat:curd5j6arfasfmzfzk5fbsfw4u
JSCN: Joint Spectral Convolutional Network for Cross Domain Recommendation
[article]
2019
arXiv
pre-print
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In this paper, we propose a Joint Spectral Convolutional Network (JSCN) for cross-domain recommendation. ...
Cross-domain recommendation can alleviate the data sparsity problem in recommender systems. ...
. • JSCN-α: Joint Spectral Convolution Network is our proposed model to learn a cross-domain recommender system. ...
arXiv:1910.08219v1
fatcat:3wyulfy6ffexlo6zac3o2gbetm
DDTCDR: Deep Dual Transfer Cross Domain Recommendation
[article]
2019
arXiv
pre-print
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Cross domain recommender systems have been increasingly valuable for helping consumers identify the most satisfying items from different categories. ...
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 make it advantageous to model domains separately. • CoNet [6] Collaborative Cross Networks (CoNet) enables knowledge transfer across domains by cross connections between base networks. • NCF [5] ...
arXiv:1910.05189v1
fatcat:y5mqqv3gebgqbgakxk4qzubgmq
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
Cross domain recommender systems have been increasingly valuable for helping consumers identify useful items in different applications. ...
cross-domain recommendation performance. ...
.• CDFM [8] Cross Domain Factorization Machine (CDFM) proposes an extension of FMs that incorporates domain information in interaction patterns. • CoNet [12] Collaborative Cross Networks (CoNet) enables ...
arXiv:2104.08490v2
fatcat:v4tdolu45je3dohehifljaq3yq
Collaborative Self-Attention Network for Session-based Recommendation
2020
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
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To this end, we propose a novel solution, Collaborative Self-Attention Network (CoSAN) for session-based recommendation, to learn the session representation and predict the intent of the current session ...
Session-based recommendation becomes a research hotspot for its ability to make recommendations for anonymous users. ...
Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not reflect the views of National Research Foundation, Singapore. ...
doi:10.24963/ijcai.2020/355
dblp:conf/ijcai/ZhangLHMCLT20
fatcat:nyeumteitrg6dhlp7i6ehsva3m
Knowledge-aware Neural Collective Matrix Factorization for Cross-domain Recommendation
[article]
2022
arXiv
pre-print
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Cross-domain recommendation (CDR) can help customers find more satisfying items in different domains. ...
This new dataset facilitates linking knowledge to bridge within- and cross-domain items for CDR. ...
Collaborative Cross Networks (CoNet) enables dual knowledge transfer across domains by introducing cross connections from one base network to another and vice versa. • DDTCDR [20]. ...
arXiv:2206.13255v1
fatcat:vsz7mlb3x5byxow7dxnxpv2cti
Collaborative Adversarial Learning for RelationalLearning on Multiple Bipartite Graphs
[article]
2020
arXiv
pre-print
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One effective way of modeling the multi-domain data is to learn the joint distribution of the shared entities across domains.In this paper, we propose Collaborative Adversarial Learning (CAL) that explicitly ...
Exploring relational learning on multiple bipartite graphs has been receiving attention because of its popular applications such as recommendations. ...
one network per domain and AAE+ uses one for all domains. ...
arXiv:2007.08308v1
fatcat:lr7eatbmlrg4tbdazlskfzah3q
Adversarial Learning for Cross Domain Recommendations
2022
ACM Transactions on Intelligent Systems and Technology
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are included in the cross domain recommendation model. ...
Existing cross domain recommender systems typically assume homogeneous user preferences across multiple domains to capture similarities of user-item interactions and to provide cross domain recommendations ...
interaction patterns difer suiciently to model domains separately. • CoNet [21] Collaborative Cross Networks (CoNet) enables knowledge transfer across domains by cross connections between base networks ...
doi:10.1145/3548776
fatcat:czzrkdmwbrflrh7ywhp7jd5ibu
RACRec: Review Aware Cross-Domain Recom-mendation for Fully-Cold-Start User
2020
IEEE Access
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In this paper, a review aware cross-domain recommendation algorithm, called RACRec, is proposed to address the fullycold-start problem in the field of product recommendation. ...
INDEX TERMS Cross-domain recommendation, select reviews, fully-cold-start, review aware recommendation. 55032 This work is licensed under a Creative Commons Attribution 4.0 License. ...
We actualize CoNet with python code.
2) LSCD [26] A Cross-Domain Collaborative Filtering algorithm. LSCD also extracts domain-specific and domain-shared features for users. ...
doi:10.1109/access.2020.2982037
fatcat:ntqxbsmu3vgmvcqcl4nbalpzwe
Parallel Split-Join Networks for Shared-account Cross-domain Sequential Recommendations
[article]
2021
arXiv
pre-print
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2019 pre-print arXiv:1910.02448v3 fatcat:zk4cy2re6rc5ldce5mrj5zndzuOther Versions
in multiple domains (i.e., recommendations are cross-domain). ...
In this work, we study shared account cross-domain sequential recommendation and propose Parallel Split-Join Network (PSJNet), a parallel modeling network to address the two challenges above. ...
Parallel Split-Join Networks for Shared-account Cross-domain Sequential Recommendations
Ren et al. ...
arXiv:1910.02448v4
fatcat:dkmd2mj34bbtjp7fd2tifhan3u
MiNet: Mixed Interest Network for Cross-Domain Click-Through Rate Prediction
[article]
2020
arXiv
pre-print
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Nevertheless, ads are usually displayed with natural content, which offers an opportunity for cross-domain CTR prediction. ...
In order to effectively leverage news data for predicting CTRs of ads, we propose the Mixed Interest Network (MiNet) which jointly models three types of user interest: 1) long-term interest across domains ...
[12] propose the Collaborative cross Network (CoNet) which enables dual knowledge transfer across domains by cross connections. Content-based methods utilize attributes of users or items. ...
arXiv:2008.02974v1
fatcat:lhxuowldd5apvhq5wlojgkfbkq
Towards Equivalent Transformation of User Preferences in Cross Domain Recommendation
[article]
2022
arXiv
pre-print
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Cross domain recommendation (CDR) is one popular research topic in recommender systems. ...
This paper focuses on a popular scenario for CDR where different domains share the same set of users but no overlapping items. ...
Further, the specific CDR applications include cross platform social e-commerce [5, 52] , multi-modal video recommendation [60] and cross domain collaboration recommendation [73] . ...
arXiv:2009.06884v2
fatcat:aeyzv4kotbakrirf4mdhqt6luy
RecBole 2.0: Towards a More Up-to-Date Recommendation Library
[article]
2022
arXiv
pre-print
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., sparsity, bias and distribution shift), and develop five packages accordingly: meta-learning, data augmentation, debiasing, fairness and cross-domain recommendation. ...
Furthermore, from a model perspective, we develop two benchmarking packages for Transformer-based and graph neural network (GNN)-based models, respectively. ...
Cross-domain recommendation (RecBole-CDR). Data distribution shift often occurs in cross-domain recommendation. ...
arXiv:2206.07351v2
fatcat:ybmtacowa5coddcpfapznpg5ay
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
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 ...
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. ...
CoNet: Collaborative cross networks for cross-domain recommendation. In CIKM 2018. 667–676.
[24] Guang-Neng Hu, Xin-Yu Dai, Feng-Yu Qiu, Rui Xia, Tao Li, Shu-Jian Huang, and Jia-Jun Chen. 2018. ...
arXiv:2012.00485v3
fatcat:kl4klnly75aodjudrprio6i4cm
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