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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.  ...  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]

Sapumal Ahangama, Danny Chiang-Choon Poo
2019 arXiv   pre-print
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]

Zhiwei Liu, Lei Zheng, Jiawei Zhang, Jiayu Han, Philip S. Yu
2019 arXiv   pre-print
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]

Pan Li, Alexander Tuzhilin
2019 arXiv   pre-print
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]

Pan Li, Alexander Tuzhilin
2021 arXiv   pre-print
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

Anjing Luo, Pengpeng Zhao, Yanchi Liu, Fuzhen Zhuang, Deqing Wang, Jiajie Xu, Junhua Fang, Victor S. Sheng
2020 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  
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]

Li Zhang, Yan Ge, Jun Ma, Jianmo Ni, Haiping Lu
2022 arXiv   pre-print
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]

Jingchao Su and Xu Chen and Ya Zhang and Siheng Chen and Dan Lv and Chenyang Li
2020 arXiv   pre-print
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

Pan Li, Brian Brost, Alexander Tuzhilin
2022 ACM Transactions on Intelligent Systems and Technology  
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

Yaru Jin, Shoubin Dong, Yong Cai, Jinlong Hu
2020 IEEE Access  
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]

Wenchao Sun and Muyang Ma and Pengjie Ren and Yujie Lin and Zhumin Chen and Zhaochun Ren and Jun Ma and Maarten de Rijke
2021 arXiv   pre-print
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]

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.  ...  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]

Xu Chen and Ya Zhang and Ivor Tsang and Yuangang Pan and Jingchao Su
2022 arXiv   pre-print
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]

Wayne Xin Zhao, Yupeng Hou, Xingyu Pan, Chen Yang, Zeyu Zhang, Zihan Lin, Jingsen Zhang, Shuqing Bian, Jiakai Tang, Wenqi Sun, Yushuo Chen, Lanling Xu (+7 others)
2022 arXiv   pre-print
., 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]

Muyang Ma and Pengjie Ren and Zhumin Chen and Zhaochun Ren and Lifan Zhao and Jun Ma and Maarten de Rijke
2020 arXiv   pre-print
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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