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Research Commentary on Recommendations with Side Information: A Survey and Research Directions [article]

Zhu Sun, Qing Guo, Jie Yang, Hui Fang, Guibing Guo, Jie Zhang, Robin Burke
2019 arXiv   pre-print
One involves the different methodologies of recommendation: the memory-based methods, latent factor, representation learning, and deep learning models.  ...  Recommender systems have become an essential tool to help resolve the information overload problem in recent decades.  ...  Central Universities in China under Grant No.  ... 
arXiv:1909.12807v2 fatcat:2nj4crzcd5attidhd3kneszmki

Taming Distrust in the Decentralized Internet with PIXIU [article]

Yubin Xia, Qingyuan Liu, Cheng Tan, Jing Leng, Shangning Xu, Binyu Zang, Haibo Chen
2019 arXiv   pre-print
In PIXIU, we design and utilize trust-{\lambda} and decentralized executor to achieve the above-needed properties.  ...  In this paper, we analyze the distrust using a simple model and highlight the properties required to faithfully accomplish one task in a decentralized Internet.  ...  Hence, PIXIU can leverage these systems to build our differential privacy trust-λ.  ... 
arXiv:1901.06095v1 fatcat:dw6e2o7swfc6hoajvwznbx3kmy

Survey for Trust-aware Recommender Systems: A Deep Learning Perspective [article]

Manqing Dong, Feng Yuan, Lina Yao, Xianzhi Wang, Xiwei Xu, Liming Zhu
2020 arXiv   pre-print
We focus on the work based on deep learning techniques, an emerging area in the recommendation research.  ...  This survey provides a systemic summary of three categories of trust-aware recommender systems: social-aware recommender systems that leverage users' social relationships; robust recommender systems that  ...  This survey aims to review the trust issue in recommender systems from a deep learning perspective to fill the gap.  ... 
arXiv:2004.03774v2 fatcat:q7mehir7hbbzpemw3q5fkby5ty

A Trust-aware Neural Collaborative Filtering for E-learning Recommendation

Xiaoyi Deng, Hailin Li, Feifei Huangfu
2018 Educational Sciences: Theory & Practice  
the performance of e-learning recommendation that aim to mitigate information overload and provide users with the most attractive and relevant learning resources.  ...  We first ties deep neural network and collaborative topic regression together, to perform users and resources latent factors learning from resource content information and users rating data.  ...  Recently, deep neural network (DNN) models show great potential for in image processing, natural language processing and recommender systems (Liu et al., 2017) .  ... 
doi:10.12738/estp.2018.5.121 fatcat:y2gmhq6ymngilcv4fpl5jmag6a

Is distrust the negation of trust?

Jiliang Tang, Xia Hu, Huan Liu
2014 Proceedings of the 25th ACM conference on Hypertext and social media - HT '14  
In this paper, we design two computational tasks by leveraging data mining and machine learning techniques to enable the computational understanding of distrust with social media data.  ...  We learn from social sciences that, as the conceptual counterpart of trust, distrust could be as important as trust. However, little work exists in studying distrust in social media.  ...  Acknowledgments The work is, in part, supported by Army Research Office (#025071) and The Office of Naval Research (N000141410095).  ... 
doi:10.1145/2631775.2631793 dblp:conf/ht/TangHL14 fatcat:epxp4mvp6rbm3i4ozik233ujsq

Technology-Enabled Disinformation: Summary, Lessons, and Recommendations [article]

John Akers, Gagan Bansal, Gabriel Cadamuro, Christine Chen, Quanze Chen, Lucy Lin, Phoebe Mulcaire, Rajalakshmi Nandakumar, Matthew Rockett, Lucy Simko, John Toman, Tongshuang Wu, Eric Zeng (+2 others)
2019 arXiv   pre-print
In this report, we summarize the space of technology-enabled mis- and disinformation based on our investigations, and then surface our lessons and recommendations for technologists, researchers, platform  ...  Allen School in the fall of 2018.  ...  Detection approaches commonly leverage machine learning to find differences between human users and bot accounts.  ... 
arXiv:1812.09383v2 fatcat:76dnmu7jojairmqnccauukmagq

An Efficient Adaptive Attention Neural Network for Social Recommendation

Munan LI, Kenji Tei, Fukazawa Yoshiaki.
2020 IEEE Access  
For example, a user may trust a friend in "travel" but distrust this friend in "music" because this friend had more travel experiences.  ...  In addition, we also utilize network embedding to learn more efficient features of each user and incorporate these features into the ANSR to enhance the recommendation results.  ...  The recommendation system can not only recommend the items that the user is interested in but also explain the reasons for the recommendation to improve the user's trust in the recommendation system.  ... 
doi:10.1109/access.2020.2984340 fatcat:enk5xziaarajlmmkl6hw6mz2ei

A Novel Implicit Trust Recommendation Approach for Rating Prediction

Yakun Li, Jiaomin Liu, Jiadong Ren, Yixin Chang
2020 IEEE Access  
Specifically, user trust neighbor set that has similar preference and taste with a target user is first obtained by trust expansion strategy via user trust diffusion features in a trust network.  ...  To solve this problem, a new implicit trust recommendation approach (ITRA) is proposed to generate item rating prediction by mining and utilizing user implicit information in recommender systems.  ...  ACKNOWLEDGMENTS The authors thank the valuable comments and suggestions of the reviewers.  ... 
doi:10.1109/access.2020.2997040 fatcat:tgmjch3dpvautdz7i2zata2pli

Towards Deep Learning Prospects: Insights for Social Media Analytics

Malik Khizar Hayat, Ali Daud, Abdulrahman A. Alshdadi, Ameen Banjar, Rabeeh Ayaz Abbasi, Yukun Bao, Hussain Dawood
2019 IEEE Access  
INDEX TERMS Social media data, dynamic network, deep learning, feature learning. 36958 He has published about 70 papers in reputed international Impact Factor journals and conferences.  ...  Deep learning (DL) has attracted increasing attention on account of its significant processing power in tasks, such as speech, image, or text processing.  ...  Trust metrics have to play a significant role in recommender systems [81] . Nevertheless, disseminating trusted information can be helpful for to conquer unswerving recommendations. Deng et al.  ... 
doi:10.1109/access.2019.2905101 fatcat:65mxyey3frdrfngvbfnfss3gpa

A Lean Innovation Model To Help Organizations Leverage Innovation For Economic Value: A Proposal

Terry Frederick, Than Lam, Vicki Martin
2014 International Journal of Management & Information Systems  
The model intends to address two main questions: 1) what are the best innovation transformation approaches for an organization to leverage innovation and 2) how can an organization effectively unleash  ...  This paper introduces a Lean Innovation Model for transforming an organization into one that leverages innovation for economic value.  ...  E-mail: tfrederick57@gmail.com Than Lam has 28 years of experience in Systems and Software Engineering and Management Information Systems. Dr.  ... 
doi:10.19030/ijmis.v18i2.8491 fatcat:3agksaxgobeuxcmtuvljgsweym

High-Trust Leadership and Blended Learning in the Age of Disruptive Innovation: Strategic Thinking for Colleges and Schools of Education

Denise Holland, Randy Piper
2016 Journal of Leadership Education  
We introduce diverse definitions of leadership and its evolutionary history and then we integrate this idea network: strategic thinking, high-trust leadership, blended learning, and disruptive innovation  ...  We conclude by recommending the special leadership role that colleges and schools of education play in sustaining democracy.  ...  Table 2 shows two solution sets for enhancing trust via communication, as recommended by Hurley.  ... 
doi:10.12806/v15/i2/t2 fatcat:caeclgmhyjhitkelyggcuyq4gu

Research of trust in virtual network relationships: the case of tourism sector
Pasitikėjimo tyrimas virtualiuose tinkliniuose ryšiuose: turizmo sektoriaus atvejis

Sonata Staniulienė
2016 Organizacijų Vadyba: Sisteminiai Tyrimai  
In addition, the article analyses data, originating from websites and social networks on the development of trust relations, in the context of virtual networking in tourism sector.  ...  The research explains how virtual network relationships among newcomers and partners form and maintain their trust beliefs about the companies they deal with in network relationships.  ...  , and recommendations of trust developing in business network relationships.  ... 
doi:10.7220/mosr.2335.8750.2016.76.8 fatcat:2mlawpry6ffydpueyebc4onvpa

Novelty Learning via Collaborative Proximity Filtering [article]

Arun Kumar, Paul Schrater
2016 arXiv   pre-print
When systems do not track and adapt to users' tastes, users lose confidence and trust, increasing the risk of user churn.  ...  The vast majority of recommender systems model preferences as static or slowly changing due to observable user experience.  ...  The learned policy is useful to proactively understand user behavior and take action before they loose trust in system and quit.  ... 
arXiv:1610.06633v1 fatcat:kl3avtbutfff7pnsy2ad42oq7y

Context-Aware Recommender Systems for Social Networks: Review, Challenges and Opportunities

Areej Bin Suhaim, Jawad Berri
2021 IEEE Access  
Her research interests include recommender system, information retrieval, social network, data mining and machine learning.  ...  Our focus is to investigate approaches and techniques used in the development of context-aware recommender systems for social networks and identify the research gaps, challenges, and opportunities in this  ...  ACKNOWLEDGEMENTS This work was supported by the Research Center of College of Computer and Information Sciences, King Saud University. The authors are grateful for this support.  ... 
doi:10.1109/access.2021.3072165 fatcat:i3igbxd44jhrzcyvynevpidcwq

Trustworthy AI Inference Systems: An Industry Research View [article]

Rosario Cammarota, Matthias Schunter, Anand Rajan, Fabian Boemer, Ágnes Kiss, Amos Treiber, Christian Weinert, Thomas Schneider, Emmanuel Stapf, Ahmad-Reza Sadeghi, Daniel Demmler, Huili Chen (+16 others)
2020 arXiv   pre-print
For example, we highlight opportunities and challenges in AI systems using trusted execution environments combined with more recent advances in cryptographic techniques to protect data in use.  ...  We continue by elaborating on the relationship between Intellectual Property (IP) and private data protection in such systems.  ...  Proofs-of-concept have appeared in the context of AI inference, specifically in Deep Learning (DL) [16, 18, 51, 67, 75] . Hybrid Solutions.  ... 
arXiv:2008.04449v1 fatcat:2loel5z6wnhabolegry4gff7oq
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