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  • Influence
Fake News Detection on Social Media: A Data Mining Perspective
TLDR
This survey presents a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets, and future research directions for fake news detection on socialMedia. Expand
Exploring temporal effects for location recommendation on location-based social networks
TLDR
A novel location recommendation framework is introduced, based on the temporal properties of user movement observed from a real-world LBSN dataset, which exhibits the significance of temporal patterns in explaining user behavior, and demonstrates their power to improve location recommendation performance. Expand
gSCorr: modeling geo-social correlations for new check-ins on location-based social networks
TLDR
This paper proposes a geo-social correlation model to capture social correlations on LBSNs considering social networks and geographical distance, and demonstrates that this approach properly models the social correlations of a user's new check-ins by considering various correlation strengths and correlation measures. Expand
Heterogeneous Network Embedding via Deep Architectures
TLDR
It is demonstrated that the rich content and linkage information in a heterogeneous network can be captured by a multi-resolution deep embedding function, so that similarities among cross-modal data can be measured directly in a common embedding space. Expand
Graph Neural Networks for Social Recommendation
TLDR
This paper provides a principled approach to jointly capture interactions and opinions in the user-item graph and proposes the framework GraphRec, which coherently models two graphs and heterogeneous strengths for social recommendations. Expand
Feature Selection
TLDR
This survey revisits feature selection research from a data perspective and reviews representative feature selection algorithms for conventional data, structured data, heterogeneous data and streaming data, and categorizes them into four main groups: similarity- based, information-theoretical-based, sparse-learning-based and statistical-based. Expand
Feature selection for classification: A review
TLDR
The growth of the high-throughput technologies has resulted in exponential growth in the harvested data with respect to both dimensionality and sample size, resulting in efficient and effective management of these data becomes increasing challenging. Expand
Exploring Social-Historical Ties on Location-Based Social Networks
TLDR
A social-historical model is proposed to explore user’s check-in behavior on location-based social networks and shows how social and historical ties can help location prediction. Expand
A Survey on Dialogue Systems: Recent Advances and New Frontiers
TLDR
This article generally divides existing dialogue systems into task-oriented and nontask- oriented models, then detail how deep learning techniques help them with representative algorithms and finally discusses some appealing research directions that can bring the dialogue system research into a new frontier. Expand
mTrust: discerning multi-faceted trust in a connected world
TLDR
Experimental results on real-world data from Epinions and Ciao show that the work of discerning multi-faceted trust can be applied to improve the performance of tasks such as rating prediction, facet-sensitive ranking, and status theory. Expand
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