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Cross-domain collaboration recommendation

Jie Tang, Sen Wu, Jimeng Sun, Hang Su
2012 Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '12  
: cross-domain collaborators often have different expertise and interest; 3) topic skewness: cross-domain collaboration topics are focused on a subset of topics.  ...  For handling topic skewness, CTL only models relevant topics to the cross-domain collaboration. We compare CTL with several baseline approaches on large publication datasets from different domains.  ...  We precisely define the problem and present three models for ranking and recommending potential collaborators.  ... 
doi:10.1145/2339530.2339730 dblp:conf/kdd/TangWSS12 fatcat:xv7kgpqrl5havjla5qshlzswoa

Cross-Domain Collaborative Filtering via Translation-based Learning [article]

Dimitrios Rafailidis
2019 arXiv   pre-print
In this paper, we propose a Cross-Domain collaborative filtering model following a Translation-based strategy, namely CDT.  ...  The main challenge of cross-domain recommendation is to weigh and learn users' different behaviors in multiple domains.  ...  Cross-Domain collaborative filtering with FMs, presented in [4] , is a stateof-the-art cross-domain recommendation.  ... 
arXiv:1908.06169v1 fatcat:xvblxtn4cjalzafcuk6e67jo2a

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.  ...  The cross-domain recommendation technique is an effective way of alleviating the data sparse issue in recommender systems by leveraging the knowledge from relevant domains.  ...  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

Fuzzy AHP and TOPSIS in Cross Domain Collaboration Recommendation with Fuzzy Visualization Representation

Maslina Zolkepli, Dept. of Computer Science, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia, Teh Noranis Mohd. Aris
2019 International Journal of Machine Learning and Computing  
method may be able to give decision makers a quick start to initiate cross-domain collaborations.  ...  The establishment of the cross domain recommendation will set a stage for efficient preparation for researchers who are interested to venture into other domains to increase their research competency.  ...  Lastly, rank the preference order for cross domain recommendation for different weights. 4) Part IV -Visualization of the cross domain recommendation ranking to the users Through the visualization system  ... 
doi:10.18178/ijmlc.2019.9.6.882 fatcat:the7vnmnsvc7tddly3ugkqebpe

Fuzzy AHP and TOPSIS in Cross Domain Collaboration Recommendation with Fuzzy Visualization Representation

Maslina Zolkepli, Dept. of Computer Science, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia, Teh Noranis Mohd. Aris
2020 International Journal of Machine Learning and Computing  
method may be able to give decision makers a quick start to initiate cross-domain collaborations.  ...  The establishment of the cross domain recommendation will set a stage for efficient preparation for researchers who are interested to venture into other domains to increase their research competency.  ...  COMBINATION OF FUZZY AHP AND FUZZY TOPSIS METHOD FOR CROSS-DOMAIN COLLABORATION RECOMMENDATION Many methods are suggested for cross-domain recommendation.  ... 
doi:10.18178/ijmlc.2020.10.6.1000 fatcat:ddzbaq7rhfbhhns5uo2t34okom

CROSS DOMAIN COLLABORATIVE FILTERING RECOMMENDER USING PROBABILISTIC MATRIX FACTORIZATION

Nazima Khanam
2017 International Journal of Advanced Research in Computer Science  
This paper, focus on Probabilistic Matrix Factorization (PMF) model in Cross Domain Recommender (CDR) that outperforms on other model based Collaborative Filtering recommenders.  ...  Of late there has been considerable interest in Cross Domain RS, where we exploit knowledge from auxiliary domains which contains additional user preference data to improve recommendation on target domains  ...  Collaborative Filtering Techniques proved to be efficient in cross domain recommenders [21] .  ... 
doi:10.26483/ijarcs.v8i9.4897 fatcat:pb2ozvh6znah5l3jv5meqxyw2e

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

Factorization Machines for Data with Implicit Feedback [article]

Babak Loni, Martha Larson, Alan Hanjalic
2018 arXiv   pre-print
We also propose how to apply FM-Pair effectively on two collaborative filtering problems, namely, context-aware recommendation and cross-domain collaborative filtering.  ...  We also show that FM-Pair is significantly more effective for ranking, compared to the standard FMs model.  ...  Cross-Domain Recommendations Cross-Domain Collaborative Filtering (CDCF) methods exploit additional information from source 3 domains to improve recommendations in a target domain.  ... 
arXiv:1812.08254v1 fatcat:krbtdxyx6jeghho3ijchwvpj4a

Item Silk Road

Xiang Wang, Xiangnan He, Liqiang Nie, Tat-Seng Chua
2017 Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '17  
Existing cross-domain recommender systems are unsuitable for this task since they have either focused on homogeneous information domains or assumed that users are fully overlapped.  ...  Towards this end, we present a novel Neural Social Collaborative Ranking (NSCR) approach, which seamlessly sews up the user-item interactions in information domains and user-user connections in SNSs.  ...  Acknowledgement We would like to thank the anonymous reviewers for their valuable comments.  ... 
doi:10.1145/3077136.3080771 dblp:conf/sigir/Wang0NC17 fatcat:kqipy2xjezaq3j7mr2gms6dani

Eliciting Auxiliary Information for Cold Start User Recommendation: A Survey

Nor Aniza Abdullah, Rasheed Abubakar Rasheed, Mohd Hairul Nizam Md. Nasir, Md Mujibur Rahman
2021 Applied Sciences  
If there are no ratings for a certain user or item, it is said that there is a cold start problem, which leads to unreliable recommendations.  ...  learning prediction models.  ...  models for cross-domain collaborative filtering in bridging between items liked by users in different domains.  ... 
doi:10.3390/app11209608 fatcat:foxbu3gt4fdxhdxuhijtufkyiu

Cross-domain citation recommendation based on hybrid topic model and co-citation selection

Supaporn Tantanasiriwong, Sumanta Guha, Paul Janecek, Choochart Haruechaiyasak, Leif Azzopardi
2017 International Journal of Data Mining Modelling and Management  
This paper proposes an approach for cross-domain citation recommendation based on the Hybrid Topic Model and Co-Citation Selection.  ...  2017) Cross-domain citation recommendation based on hybrid topic model and co-citation selection citation selection. International Journal of Data Mining, Modelling and Management, 9 (3).  ...  The Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs.  ... 
doi:10.1504/ijdmmm.2017.086566 fatcat:pqoywmyf7nf3fop7pge5rf5jhi

Cross-domain citation recommendation based on hybrid topic model and co-citation selection

Leif Azzopardi, Choochart Haruechaiyasak, Sumanta Guha, Paul Janecek, Supaporn Tantanasiriwong
2017 International Journal of Data Mining Modelling and Management  
This paper proposes an approach for cross-domain citation recommendation based on the Hybrid Topic Model and Co-Citation Selection.  ...  2017) Cross-domain citation recommendation based on hybrid topic model and co-citation selection citation selection. International Journal of Data Mining, Modelling and Management, 9 (3).  ...  The Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs.  ... 
doi:10.1504/ijdmmm.2017.10007657 fatcat:6belfatx5fh57d742cufaf5ccm

Discovering Both Explicit and Implicit Similarities for Cross-Domain Recommendation [chapter]

Quan Do, Wei Liu, Fang Chen
2017 Lecture Notes in Computer Science  
In this paper, we propose a cross-domain recommender as the first algorithm utilizing both explicit and implicit similarities between datasets across sources for performance improvement.  ...  Recommender System has become one of the most important techniques for businesses today. Improving its performance requires a thorough understanding of latent similarities among users and items.  ...  [4] to propose CLFM for cross-domain recommendation.  ... 
doi:10.1007/978-3-319-57529-2_48 fatcat:arflxmeoxvafnnxfkh5shrvyfa

Cross-domain Recommendations for Personalized Semantic Services

Hla Hla Moe, Win Thanda Aung
2012 International Journal of Computer Applications Technology and Research  
This paper tends to provide cross-domain recommendations for personalized semantic services using Taxonomic CCBR, directed acyclic graph by Ford-Fulkerson algorithm and TOPSIS method.  ...  Among them, cross-domain recommendation is an emerging research topic and in this field, it is important to investigate how to manage personalization and how to consider customer's contextual features  ...  In this paper, a framework for cross-domain recommender system is proposed.  ... 
doi:10.7753/ijcatr0201.1015 fatcat:bxhjdqqmvfco3fh46kfx4cf4he

Video recommendation over multiple information sources

Xiaojian Zhao, Jin Yuan, Meng Wang, Guangda Li, Richang Hong, Zhoujun Li, Tat-Seng Chua
2012 Multimedia Systems  
Second, based on multiple ranking lists, a multi-task rank aggregation approach is proposed to integrate these ranking lists to generate a final result for video recommendation.  ...  Video recommendation is an important tool to help people access interesting videos. In this paper, we propose a universal scheme to integrate rich information for personalized video recommendation.  ...  For ''Rating'', the ranking list is generated based Collaborative filtering-based ranking Collaborative filtering is the most widely adopted approach for video recommendation.  ... 
doi:10.1007/s00530-012-0267-z fatcat:vu2jxkwobjcshf3evbf4pxwxle
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