Filters








15 Hits in 1.4 sec

DDTCDR: Deep Dual Transfer Cross Domain Recommendation [article]

Pan Li, Alexander Tuzhilin
2019 arXiv   pre-print
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  ...  METHOD In this section, we present the proposed Deep Dual Transfer Cross Domain Recommendation (DDTCDR) model.  ... 
arXiv:1910.05189v1 fatcat:y5mqqv3gebgqbgakxk4qzubgmq

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

Pan Li, Alexander Tuzhilin
2021 arXiv   pre-print
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 recommendation performance.  ...  . • Fig. 1 : 1 Dual Metric Learning for Cross-Domain Recommendations Fig. 2 : 2 Dual Metric Learning • DDTCDR [14]Deep Dual Transfer Cross Domain Recommendation (DDTCDR) efficiently transfers user  ... 
arXiv:2104.08490v2 fatcat:v4tdolu45je3dohehifljaq3yq

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.  ...  Deep Dual Transfer Cross Domain Recom- mendation (DDTCDR) learns latent orthogonal mappings across domains and provides cross domain recommendations by leveraging user preferences from all domains.  ... 
arXiv:2206.13255v1 fatcat:vsz7mlb3x5byxow7dxnxpv2cti

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.  ...  This linear mapping strategy is also used in collaborative cross networks (CoNet) (Hu et al., 2018) and deep dual transfer cross domain recommendation (DDTCDR) (Li and Tuzhilin, 2020) .  ... 
arXiv:2101.05611v2 fatcat:t3aj3eakfrgnpjd3t5wurefd2a

A cross-domain recommender system using deep coupled autoencoders [article]

Alexandros Gkillas, Dimitrios Kosmopoulos
2022 arXiv   pre-print
In light of this scenario, two novel coupled autoencoder-based deep learning methods are proposed for cross-domain recommendation.  ...  Cross-domain recommendation as a domain adaptation framework has been utilized to efficiently address these challenging issues, by exploiting information from multiple domains.  ...  In more details, DDTCDR exploits the merits of the dual transfer learning and the feature embedding method to transfer knowledge across domains.  ... 
arXiv:2112.07617v3 fatcat:3g6kd7j23rgohab4j355jzyu4a

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.  ...  The majority of recent methods have explored the shared-user representation to transfer knowledge across domains.  ...  Motivated by dual learning [78] , Li et al. [49] introduced a deep dual transfer network named DDTCDR to enhance the bidirectional knowledge in CDR.  ... 
arXiv:2009.06884v2 fatcat:aeyzv4kotbakrirf4mdhqt6luy

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  ...  In this paper, we focus on three new scenarios, i.e., Dual-Target CDR (DTCDR), Multi-Target CDR (MTCDR), and CDR+CSR, and aim to improve the recommendation accuracy in all datasets simultaneously for all  ...  In addition, in [26] , Li et al. proposed the DDTCDR, a deep dual-transfer framework for dual-target CDR.  ... 
arXiv:2108.07976v1 fatcat:gfie4f5b4ncuvotz7wiqlhaice

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  ...  Overview of the ACDR model for producing cross domain recommendations in domain T . 4. 2 . 1 21 Cross Domain Recommendation Models.• DDTCDR [32] Deep Dual Transfer Cross Domain Recommendation (DDTCDR  ... 
doi:10.1145/3548776 fatcat:czzrkdmwbrflrh7ywhp7jd5ibu

Deep Unified Representation for Heterogeneous Recommendation [article]

Chengqiang Lu, Mingyang Yin, Shuheng Shen, Luo Ji, Qi Liu, Hongxia Yang
2022 arXiv   pre-print
To tackle this problem, we propose a kernel-based neural network, namely deep unified representation (or DURation) for heterogeneous recommendation, to jointly model unified representations of heterogeneous  ...  Previous works mainly focus on homogeneous recommendation and little progress has been made for heterogeneous recommender systems.  ...  Cross-domain Recommendation A considerable amount of literature has been published on the problem of cross-domain recommendation systems [2, 8, 26] .  ... 
arXiv:2201.05861v1 fatcat:36ee4wjdxnegzep4aeakdtktja

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

Tianzi Zang, Yanmin Zhu, Haobing Liu, Ruohan Zhang, Jiadi Yu
2022 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.  ...  The Deep Dual Knowledge Transfer method realizes the bidirectional deep transfer of knowledge between domains.  ... 
arXiv:2108.03357v2 fatcat:ywwh44x3pfbnbesy5ojogg4hyy

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
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.  ...  Cross-domain sequential recommendation is the task of predict the next item that the user is most likely to interact with based on past sequential behavior from multiple domains.  ...  Neural attentive session-based recommendation. In CIKM 2017. 1419–1428. [33] Pan Li and Alexander Tuzhilin. 2019. DDTCDR: Deep dual transfer cross domain recommendation.  ... 
arXiv:2012.00485v3 fatcat:kl4klnly75aodjudrprio6i4cm

Multi-Sparse-Domain Collaborative Recommendation via Enhanced Comprehensive Aspect Preference Learning [article]

Xiaoyun Zhao, Ning Yang, Philip S. Yu
2022
To overcome these issues, we propose a Multi-Sparse-Domain Collaborative Recommendation (MSDCR) model for multi-target cross-domain recommendation.  ...  Cross-domain recommendation (CDR) has been attracting increasing attention of researchers for its ability to alleviate the data sparsity problem in recommender systems.  ...  DDTCDR: Deep dual transfer cross domain [26] Cheng Zhao, Chenliang Li, and Cong Fu. 2019. Cross-domain recommendation recommendation.  ... 
doi:10.48550/arxiv.2201.05973 fatcat:hp2xl7o6pfg2xhqt5zn4panp7e

Cross-Domain Recommendation: Challenges, Progress, and Prospects

Feng Zhu, Yan Wang, Chaochao Chen, Jun Zhou, Longfei Li, Guanfeng Liu
2021 Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence   unpublished
To address the long-standing data sparsity problem in recommender systems (RSs), cross-domain recommendation (CDR) has been proposed to leverage the relatively richer information from a richer domain to  ...  Specifically, we first summarize existing CDR approaches into four types, including single-target CDR, single-target multi-domain recommendation (MDR), dual-target CDR, and multi-target CDR.  ...  Motivation of Cross-Domain Recommendations.  ... 
doi:10.24963/ijcai.2021/639 fatcat:glergfn675h6zape36gxyxvqgi

TrNews: Heterogeneous User-Interest Transfer Learning for News Recommendation

Guangneng Hu, Qiang Yang
2021 Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume   unpublished
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.  ...  This linear mapping strategy is also used in collaborative cross networks (CoNet) (Hu et al., 2018) and deep dual transfer cross domain recommendation (D-DTCDR) (Li and Tuzhilin, 2020) .  ... 
doi:10.18653/v1/2021.eacl-main.62 fatcat:7ef7urazzzaqhi3kzutxg6rpsa

Recommender systems based on graph embedding techniques: A review

Yue Deng
2022 IEEE Access  
As for alleviating the sparsity and cold start problems encountered by recommender systems, researchers generally resort to employing side information or knowledge in recommendation as a strategy for uncovering  ...  In addition, after comparing several representative graph embedding-based recommendation models with the most common-used conventional recommendation models on simulations, this article manifests that  ...  back and forth by means of dual transfer learning [222] - [224] .  ... 
doi:10.1109/access.2022.3174197 fatcat:s267xaasovh6ffaomi7l32pqyi