Modeling Personality Effect in Trust Prediction

Trust among users in online social networks is a key factor in determining the amount of information that is perceived as reliable. Compared to the number of users in online social networks, user-specified trust relations are very sparse. This makes the pair-wise trust prediction a challenging task. Social studies have investigated trust and why people trust each other. The relation between trust and personality traits of people who established those relations, has been proved by social theories. In this work, we attempt to alleviate the effect of the sparsity of trust relations by extracting implicit information from the users, in particular, by focusing on users' personality traits and seeking a low-rank representation of users. We investigate the potential impact on the prediction of trust relations, by incorporating users' personality traits based on the Big Five factor personality model. We evaluate the impact of similarities of users' personality traits and the effect of each personality trait on pair-wise trust relations. Next, we formulate a new unsupervised trust prediction model based on tensor decomposition. Finally, we empirically evaluate this model using two real-world datasets. Our extensive experiments confirm the superior performance of our model compared to the state-of-the-art approaches.

Download Full-text

Trust Prediction for Online Social Networks with Integrated Time-Aware Similarity

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3447682 ◽

2021 ◽

Vol 15 (6) ◽

pp. 1-30

Author(s):

Xiaofeng Gao ◽

Wenyi Xu ◽

Mingding Liao ◽

Guihai Chen

Keyword(s):

Social Networks ◽

Matrix Factorization ◽

Online Social Networks ◽

User Similarity ◽

Trust Prediction ◽

Efficiency And Effectiveness ◽

Effectiveness And Efficiency ◽

Real World Datasets ◽

Prediction Approach ◽

Time Aware

Online social networks gain increasing popularity in recent years. In online social networks, trust prediction is significant for recommendations of high reputation users as well as in many other applications. In the literature, trust prediction problem can be solved by several strategies, such as matrix factorization, trust propagation, and -NN search. However, most of the existing works have not considered the possible complementarity among these mainstream strategies to optimize their effectiveness and efficiency. In this article, we propose a novel trust prediction approach named iSim : an integrated time-aware similarity-based collaborative filtering approach leveraging on user similarity, which integrates three kinds of factors to measure user similarity, including vector space similarity, time-aware matrix factorization, and propagated trust. This article is the first work in the literature employing time-aware matrix factorization and propagated trust in the study of similarity. Additionally, we use several methods like adding inverted index to reduce the time complexity of iSim , and provide its theoretical time bound. Moreover, we also provide the detailed overview and theoretical analysis of the existing works. Finally, the extensive experiments with real-world datasets show that iSim achieves great improvement for both efficiency and effectiveness over the state-of-the-art approaches.

Download Full-text

Robust Tensor Decomposition via Orientation Invariant Tubal Nuclear Norms

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i04.6074 ◽

2020 ◽

Vol 34 (04) ◽

pp. 6102-6109 ◽

Cited By ~ 2

Author(s):

Andong Wang ◽

Chao Li ◽

Zhong Jin ◽

Qibin Zhao

Keyword(s):

Estimation Error ◽

Tensor Decomposition ◽

Low Rank ◽

Superior Performance ◽

Orientation Sensitivity ◽

Asymptotic Error ◽

Rank Tensor ◽

Tensor Recovery ◽

Decomposition Models ◽

Real World Datasets

Low-rank tensor recovery has been widely applied to computer vision and machine learning. Recently, tubal nuclear norm (TNN) based optimization is proposed with superior performance as compared to other tensor nuclear norms. However, one major limitation is its orientation sensitivity due to low-rankness strictly defined along tubal orientation and it cannot simultaneously model spectral low-rankness in multiple orientations. To this end, we introduce two new tensor norms called OITNN-O and OITNN-L to exploit multi-orientational spectral low-rankness for an arbitrary K-way (K ≥ 3) tensors. We further formulate two robust tensor decomposition models via the proposed norms and develop two algorithms as the solutions. Theoretically, we establish non-asymptotic error bounds which can predict the scaling behavior of the estimation error. Experiments on real-world datasets demonstrate the superiority and effectiveness of the proposed norms.

Download Full-text

AtNE-Trust: Attributed Trust Network Embedding for Trust Prediction in Online Social Networks

2020 IEEE International Conference on Data Mining (ICDM) ◽

10.1109/icdm50108.2020.00069 ◽

2020 ◽

Author(s):

Qi Wang ◽

Weiliang Zhao ◽

Jian Yang ◽

Jia Wu ◽

Chuan Zhou ◽

...

Keyword(s):

Social Networks ◽

Online Social Networks ◽

Network Embedding ◽

Trust Network ◽

Trust Prediction

Download Full-text

The Impact of Routing Attacks on Pastry-Based P2P Online Social Networks

Lecture Notes in Computer Science - Euro-Par 2014: Parallel Processing Workshops ◽

10.1007/978-3-319-14325-5_30 ◽

2014 ◽

pp. 347-358 ◽

Cited By ~ 1

Author(s):

Felix A. Eichert ◽

Markus Monhof ◽

Kalman Graffi

Keyword(s):

Social Networks ◽

Online Social Networks ◽

Routing Attacks ◽

The Impact

Download Full-text

Some results of studying the impact of digital technologies upon learning activities

Педагогика и просвещение ◽

10.7256/2454-0676.2020.1.31995 ◽

2020 ◽

pp. 1-14

Author(s):

Elvira Vitaljevna Burtseva ◽

Olga Chepak ◽

Olga Kulikova

Keyword(s):

Higher Education ◽

Social Networks ◽

Negative Impact ◽

Digital Technologies ◽

Educational Process ◽

Learning Activities ◽

Key Factor ◽

Negative Effect ◽

The Subject ◽

The Impact

The subject of this research is the implementation of digital technologies in educational process of a university. The goal consists in studying the impact of digital technologies upon the students’ learning activities. The article presents the results of questionnaire-based survey among students by the three question pools. In the course of research, the author examines such aspects of the problem, as the positive and negative impact of technologies upon learning activities of the students of digital generation. Particular attention is given to consideration of students’ attitude on digitalization of higher education. The opinions of pedagogues on the results of conducted research are presented. The scientific novelty lies in mainstreaming the question on the negative impact of digital technologies upon learning activities of the modern generation of students that deserves special attention. On the background of common passion of the scholars of researchers and pedagogues for the ideas of digitalization of education, when digital technologies are viewed as virtually the key factor for modernization of educational process; second come the problems of growing pathological dependence of youth on digital technologies, undesired to switch to digitalized educational process to the disadvantage of communication in social networks and pleasant pastime online. The problem of the negative effect of digital technologies on learning activities must be recognized in order to find the ways for its solution.

Download Full-text

Power-law Distribution Aware Trust Prediction

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/495 ◽

2018 ◽

Cited By ~ 1

Author(s):

Xiao Wang ◽

Ziwei Zhang ◽

Jing Wang ◽

Peng Cui ◽

Shiqiang Yang

Keyword(s):

Power Law ◽

Low Rank ◽

Superior Performance ◽

Power Law Distribution ◽

Trust Network ◽

Trust Prediction ◽

Trust Networks ◽

The Arts ◽

Proof Of Correctness ◽

Closure Structure

Trust prediction, aiming to predict the trust relations between users in a social network, is a key to helping users discover the reliable information. Many trust prediction methods are proposed based on the low-rank assumption of a trust network. However, one typical property of the trust network is that the trust relations follow the power-law distribution, i.e., few users are trusted by many other users, while most tail users have few trustors. Due to these tail users, the fundamental low-rank assumption made by existing methods is seriously violated and becomes unrealistic. In this paper, we propose a simple yet effective method to address the problem of the violated low-rank assumption. Instead of discovering the low-rank component of the trust network alone, we learn a sparse component of the trust network to describe the tail users simultaneously. With both of the learned low-rank and sparse components, the trust relations in the whole network can be better captured. Moreover, the transitive closure structure of the trust relations is also integrated into our model. We then derive an effective iterative algorithm to infer the parameters of our model, along with the proof of correctness. Extensive experimental results on real-world trust networks demonstrate the superior performance of our proposed method over the state-of-the-arts.

Download Full-text

C-DeepTrust: A Context-Aware Deep Trust Prediction Model in Online Social Networks

IEEE Transactions on Neural Networks and Learning Systems ◽

10.1109/tnnls.2021.3107948 ◽

2021 ◽

pp. 1-14

Author(s):

Qi Wang ◽

Weiliang Zhao ◽

Jian Yang ◽

Jia Wu ◽

Shan Xue ◽

...

Keyword(s):

Social Networks ◽

Prediction Model ◽

Online Social Networks ◽

Context Aware ◽

Trust Prediction

Download Full-text

Is Social Media Marketing Really Working?

E-Marketing in Developed and Developing Countries ◽

10.4018/978-1-4666-3954-6.ch011 ◽

2013 ◽

pp. 174-193 ◽

Cited By ~ 1

Author(s):

Felipe Uribe Saavedra ◽

Josep Rialp Criado ◽

Joan Llonch Andreu

Keyword(s):

Social Networks ◽

Social Media ◽

Online Social Networks ◽

Business Performance ◽

Dynamic Capabilities ◽

Social Media Marketing ◽

Marketing Tool ◽

Use Of Social Media ◽

Marketing Intensity ◽

The Impact

Online social networks have become the fastest growing phenomenon on the Internet and firms are beginning to take advantage of them as a marketing tool. However, the strategic importance of social media marketing is not yet clear, given the novelty and the difficulty of measuring its impact on business performance. This study uses data from 191 Spanish firms from several sectors to measure the impact of the intensity of use of social media marketing on the relationship between the dynamic capabilities of market orientation and entrepreneurial orientation, and business performance. The results provide evidence of the moderating effects of social media marketing intensity on the strength of the mentioned relations and the importance of a strong and committed marketing strategy on digital social networks for businesses.

Download Full-text