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A Regularization Method with Inference of Trust and Distrust in Recommender Systems [chapter]

Dimitrios Rafailidis, Fabio Crestani
2017 Lecture Notes in Computer Science  
In this study we investigate the recommendation problem with trust and distrust relationships to overcome the sparsity of users' preferences, accounting for the fact that users trust the recommendations  ...  So, we first propose an inference step with multiple random walks to predict the implicit-missing trust relationships that users might have in recommender systems, while considering users' explicit trust  ...  Dimitrios Rafailidis was supported by the COMPLEXYS and INFORTECH Research Institutes of University of Mons.  ... 
doi:10.1007/978-3-319-71246-8_16 fatcat:gcrng7g5yzdqzhtzzrpy3sf5qu

Relation Representation Learning Via Signed Graph Mutual Information Maximization for Trust Prediction

Yongjun Jing, Hao Wang, Kun Shao, Xing Huo
2021 Symmetry  
In this paper, we proposed a relation representation learning via signed graph mutual information maximization (called SGMIM).  ...  In SGMIM, we incorporate a translation model and positive point-wise mutual information to enhance the relation representations and adopt Mutual Information Maximization to align the entity and relation  ...  Acknowledgments: The authors are grateful to the editor and referees for their valuable comments and suggestions for improving the paper.  ... 
doi:10.3390/sym13010115 fatcat:7fhxnc6dnbf2vha5lcvndclvtq

TrustRec: An effective approach to exploit implicit trust and distrust relationships along with explicitones for accurate recommendations

Reyhani Hamedani, Irfan Ali, Jiwon Hong, Sang-Wook Kim
2020 Computer Science and Information Systems  
Trust-aware recommendation approaches are widely used to mitigate the cold-start problem in recommender systems by utilizing trust networks.  ...  factorization model.  ...  .: Modeling trust and distrust information in recommender systems via joint matrix 28 factorization with signed graphs. In: ACM SAC. pp. 1060-1065 (2016) 29 25.  ... 
doi:10.2298/csis200608039h fatcat:cdilbc4gence3gwwvipcmdhcmi

Modelling Trust in Semantic Web Applications

Gregory Albiston, Taha Osman, Evtim Peytchev
2014 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation  
This paper examines some of the barriers to the adoption of car-sharing, termed carpooling in the US, and develops a framework for trusted recommendations.  ...  Identification is made of potential vocabularies, ontologies and public social networks which can be used as the basis for deriving direct and indirect trust values in an implementation.  ...  Global and Local Trust Two perspectives have been take to trust in recommender systems, global and local.  ... 
doi:10.1109/uksim.2014.62 dblp:conf/uksim/AlbistonOP14 fatcat:loaizmexindzjdj626eu6fxqpm

A Community-Based Approach for Link Prediction in Signed Social Networks

Saeed Reza Shahriary, Mohsen Shahriari, Rafidah MD Noor
2015 Scientific Programming  
We were able to devise three community-based ranking algorithms which are suitable for signed graphs, and also we evaluated these ranking algorithms via sign prediction problem.  ...  Hence, we were motivated to investigate ranking algorithms availed for signed graphs and their effect on sign prediction problem.  ...  Belief Matrix Model was introduced by [5] and was proposed for predicting trust or distrust between two particular users in signed networks.  ... 
doi:10.1155/2015/602690 fatcat:5m7d6a2dsvf3dphih3ce33cgzq

Signed-PageRank: An Efficient Influence Maximization Framework for Signed Social Networks

Xiaoyan Yin, Xiao Hu, Yanjiao Chen, Xu Yuan, Baochun Li
2019 IEEE Transactions on Knowledge and Data Engineering  
We propose a new framework to characterize the information propagation process in signed social networks, which models the dynamics of individuals' beliefs and attitudes towards the advertisement based  ...  Signed social networks with both positive (friends) and negative (foes) relationships pose new challenges and opportunities, since the influence of negative relationships can be leveraged to promote information  ...  ACKNOWLEDGMENT This research was sponsored in part by National Natural Science  ... 
doi:10.1109/tkde.2019.2947421 fatcat:74cyrr7mcbgelmgwrxow6y3uci

A Comprehensive Survey on Community Detection with Deep Learning [article]

Xing Su, Shan Xue, Fanzhen Liu, Jia Wu, Jian Yang, Chuan Zhou, Wenbin Hu, Cecile Paris, Surya Nepal, Di Jin, Quan Z. Sheng, Philip S. Yu
2021 arXiv   pre-print
This survey devises and proposes a new taxonomy covering different state-of-the-art methods, including deep learning-based models upon deep neural networks, deep nonnegative matrix factorization and deep  ...  Despite the classical spectral clustering and statistical inference methods, we notice a significant development of deep learning techniques for community detection in recent years with their advantages  ...  Recommendation Systems. Community structure plays a vital role for graph-based recommendation systems [138] , [139] , as the community members may have similar interests and preferences.  ... 
arXiv:2105.12584v2 fatcat:matipshxnzcdloygrcrwx2sxr4

On the Trust and Trust Modelling for the Future Fully-Connected Digital World: A Comprehensive Study

Hannah Lim Jing Ting, Xin Kang, Tieyan Li, Haiguang Wang, Cheng-Kang Chu
2021 IEEE Access  
ACKNOWLEDGMENT We thank the editor and anonymous referees of this journal whose comments substantially improved this article.  ...  In a weighted graph, like those typically used in trust, the weights will be used directly as entries in the adjacency matrix.  ...  For trust, the adjacency matrix is most useful for illustrating, in matrix form, the total path weights (for weighted graphs) or total number of paths (for non-weighted graphs) from any one node to another  ... 
doi:10.1109/access.2021.3100767 fatcat:xvg4qli6t5eq7lmvhrjpy6tu5i

Meeting the challenge of quantitative risk assessment for genetic control techniques: a framework and some methods applied to the common Carp (Cyprinus carpio) in Australia

Keith R. Hayes, Brian Leung, Ronald Thresher, Jeffrey M. Dambacher, Geoffrey R. Hosack
2013 Biological Invasions  
These models treat the factors that determine risk as a joint probability distribution that can be factored into a series of simpler conditional distributions to allow Bayesian inference following observed  ...  The key science challenge in this context is our limited understanding of complex and highly variable ecosystems. Hierarchical models are one way to approach this complexity and heterogeneity.  ...  diverse participants in the symposium, whose insightful comments on the emerging technology greatly facilitated development of the ideas expressed in this paper.  ... 
doi:10.1007/s10530-012-0392-9 fatcat:yznsrodgp5f6bjkgfot3sohjni

Exact Graph Structure Estimation with Degree Priors

Bert Huang, Tony Jebara
2009 2009 International Conference on Machine Learning and Applications  
We show an example application to post-processing of recommender system predictions.  ...  After this mapping, the most likely graph structure can be found in cubic time with respect to the number of nodes using max flow methods.  ...  The last is trust/distrust data gathered from which represents whether users trust other users' opinions.  ... 
doi:10.1109/icmla.2009.103 dblp:conf/icmla/HuangJ09 fatcat:7bsnjhrbpfeqfm4wxwelhusbem

Sign Prediction on Unlabeled Social Networks Using Branch and Bound Optimized Transfer Learning

Weiwei Yuan, Jiali Pang, Donghai Guan, Yuan Tian, Abdullah Al-Dhelaan, Mohammed Al-Dhelaan
2019 Complexity  
With this design, the target domain label information is not required for classifier.  ...  To solve these problems, we propose a novel sign prediction on unlabeled social networks using branch and bound optimized transfer learning (SP_BBTL) sign prediction model.  ...  Because of this rich preserved information, signed networks have been widely used in many applications, such as recommender systems [1, 2] and community detection [3, 4] .  ... 
doi:10.1155/2019/4906903 fatcat:4zgncwdmdnhrpm3ux27r2ve2am

Modeling and mining of dynamic trust in complex service-oriented systems

Florian Skopik, Daniel Schall, Schahram Dustdar
2010 Information Systems  
However, finding the right partner to work on joint tasks or to solve emerging problems in such scenarios is challenging due to scale and temporary nature of collaborations.  ...  We argue that trust between members is essential for successful collaborations. Unlike a security perspective, we focus on the notion of social trust in collaborative networks.  ...  in the environment, and is therefore the adjacency matrix of the mentioned trust graph G.  ... 
doi:10.1016/ fatcat:wf6y5k255ndyjavsidu7b2rr5m

Investigating Negative Interactions in Multiplex Networks: A Mutual Information Approach [article]

Alireza Hajibagheri, Gita Sukthankar
2018 arXiv   pre-print
These interactions can be encoded as having a valence with positive links marking interactions such as trust and friendship and negative links denoting distrust or hostility.  ...  Many interesting real-world systems are represented as complex networks with multiple types of interactions and complicated dependency structures between layers.  ...  Wigand and Nitin Agarwal (University of Arkansas at Little Rock, Department of Information Science); their research was supported by the National Science Foundation and Travian Games GmbH, Munich, Germany  ... 
arXiv:1804.07210v4 fatcat:lrdn4hd2afhbzmrpbzxqj3ceim

Modeling and Mining of Dynamic Trust in Complex Service-Oriented Systems [chapter]

Florian Skopik, Daniel Schall, Schahram Dustdar
2011 Socially Enhanced Services Computing  
However, finding the right partner to work on joint tasks or to solve emerging problems in such scenarios is challenging due to scale and temporary nature of collaborations.  ...  We argue that trust between members is essential for successful collaborations. Unlike a security perspective, we focus on the notion of social trust in collaborative networks.  ...  in the environment, and is therefore the adjacency matrix of the mentioned trust graph G.  ... 
doi:10.1007/978-3-7091-0813-0_3 fatcat:5fbkqz67g5bljllro3hii6kzxy

Towards Time-Aware Context-Aware Deep Trust Prediction in Online Social Networks [article]

Seyed Mohssen Ghafari
2020 arXiv   pre-print
There are several applications for trust in Online Social Networks (OSNs), including social spammer detection, fake news detection, retweet behaviour detection and recommender systems.  ...  Trust can be defined as a measure to determine which source of information is reliable and with whom we should share or from whom we should accept information.  ...  Therefore, to employ trust information in different applications in OSNs (e.g., recommender systems and retweet behaviour prediction), we need to predict unknown trust relations among users.  ... 
arXiv:2003.09543v1 fatcat:4ghgxbpepzaaxl3ed2gies7xr4
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