scholarly journals The Impact of User Behavior on Information Diffusion in D2D Communications: A Discrete Dynamical Model

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Chenquan Gan ◽  
Xiaoke Li ◽  
Lisha Wang ◽  
Zufan Zhang
Keyword(s):  
Information Diffusion ◽  
User Behavior ◽  
Transition Process ◽  
Dynamical Model ◽  
Stability Factor ◽  
User Interest ◽  
User Influence ◽  
Proposed Model ◽  
Interaction Factor ◽  
The Impact

This paper aims to explore the impact of user behavior on information diffusion in D2D (Device-to-Device) communications. A discrete dynamical model, which combines network metrics and user behaviors, including social relationship, user influence, and interest, is proposed and analyzed. Specifically, combined with social tie and user interest, the success rate of data dissemination between D2D users is described, and the interaction factor, user influence, and stability factor are also defined. Furthermore, the state transition process of user is depicted by a discrete-time Markov chain, and global stability analysis of the proposed model is also performed. Finally, some experiments are examined to illustrate the main results and effectiveness of the proposed model.

scholarly journals Influence Model of User Behavior Characteristics on Information Dissemination

2016 ◽  
Vol 11 (2) ◽  
pp. 209 ◽  
Author(s):  
Shao Chun Han ◽  
Yun Liu ◽  
Hui Ling Chen ◽  
Zhen Jiang Zhang
Keyword(s):  
Human Behavior ◽  
Information Diffusion ◽  
Statistical Physics ◽  
Information Dissemination ◽  
User Behavior ◽  
Activity Patterns ◽  
Propagation Model ◽  
Propagation Time ◽  
Behavior Characteristics ◽  
The Impact

Quantitative analysis on human behavior, especially mining and modeling temporal and spatial regularities, is a common focus of statistical physics and complexity sciences. The in-depth understanding of human behavior helps in explaining many complex socioeconomic phenomena, and in finding applications in public opinion monitoring, disease control, transportation system design, calling center services, information recommendation. In this paper,we study the impact of human activity patterns on information diffusion. Using SIR propagation model and empirical data, conduct quantitative research on the impact of user behavior on information dissemination. It is found that when the exponent is small, user behavioral characteristics have features of many new dissemination nodes, fast information dissemination, but information continued propagation time is short, with limited influence; when the exponent is big, there are fewer new dissemination nodes, but will expand the scope of information dissemination and extend information dissemination duration; it is also found that for group behaviors, the power-law characteristic a greater impact on the speed of information dissemination than individual behaviors. This study provides a reference to better understand influence of social networking user behavior characteristics on information dissemination and kinetic effect.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

scholarly journals Channel Optimization of Marketing Based on Users’ Social Network Information

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Chaolin Peng
Keyword(s):  
Social Network ◽  
Information Diffusion ◽  
Information Technologies ◽  
Information Dissemination ◽  
Product Information ◽  
Main Idea ◽  
Optimal Solution ◽  
User Interest ◽  
Nsga Ii ◽  
The Impact

Marketing in the social network environment integrates current advanced internet and information technologies. This marketing method not only broadens marketing channels and builds a network communication platform but also meets the purchase needs of customers in the entire market and shortens customer purchases. The process is also an inevitable product of the development of the times. However, when companies use social networks for product marketing, they usually face the impact of multiple realistic factors. This article takes the maximization of influence as the main idea to find seed users for product information dissemination and also considers the users’ interest preferences. The target users can influence the product, and the company should control marketing costs to obtain a larger marginal benefit. Based on this, this paper considers factors such as the scale of information diffusion, user interest preferences, and corporate budgets, takes the influence maximization model as a multiobjective optimization problem, and proposes a multiobjective maximization of influence (MOIM) model. To solve the NP-hard problem of maximizing influence, this paper uses Monte Carlo sampling to calculate high-influence users. Next, a seed user selection algorithm based on NSGA-II is proposed to optimize the above three objective functions and find the optimal solution. We use real social network data to verify the performance of models and methods. Experiments show that the proposed model can generate appropriate seed sets and can meet different purposes of information dissemination. Sensitivity analysis proves that our model is robust under different actual conditions.

scholarly journals Rapid Recovery Control Method Based on Improved VDCOLs for Hybrid Multi-Infeed DC Transmission System After AC Failure

2021 ◽  
Vol 9 ◽  
Author(s):  
Chizu Mao ◽  
Xianchao Liu ◽  
Qing Li ◽  
Zhihua Xu ◽  
Yechun Xin ◽  
...  
Keyword(s):  
Control Method ◽  
Transition Process ◽  
Rapid Recovery ◽  
Control Mode ◽  
Strength Index ◽  
Dc Voltage ◽  
Dc Power ◽  
Interaction Factor ◽  
Commutation Failure ◽  
The Impact

Continuous commutation failure is very likely to occur in the hybrid Multi-infeed high-voltage direct current (HMIDC) after AC failure. In order to improve the recovery quality after HMIDC failure, an AC-DC voltage-dependent current order limiter (VDCOL) based on system strength index is proposed in this article. Firstly, the control mode transition process and system recovery process after DC failure are analyzed based on the hybrid multi-infeed DC transmission port model. Then, considering the impact of AC voltage and DC voltage input signals of VDCOL on AC voltage recovery and DC power recovery, respectively, the interaction factor and strength index of the hybrid multi-infeed system are constructed. Moreover, the weight coefficient of AC and DC voltage is calculated according to the strength of the multi-infeed system. Finally, a three-infeed hybrid DC transmission simulation model is built in the MATLAB/Simulink digital simulation platform. The simulation results demonstrate that the rapid recovery strategy proposed in this article can effectively suppress continuous commutation failure and improve the recovery speed of AC voltage and DC power.

scholarly journals Recommendation Based on Users’ Long-Term and Short-Term Interests with Attention

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Qiaoqiao Tan ◽  
Fang’ai Liu
Keyword(s):  
User Behavior ◽  
User Interest ◽  
Short Term ◽  
User Interests ◽  
User Behaviors ◽  
Current State ◽  
Proposed Model ◽  
Dependence Problem ◽  
Behavior Sequences

Recommendations based on user behavior sequences are becoming more and more common. Some studies consider user behavior sequences as interests directly, ignoring the mining and representation of implicit features. However, user behaviors contain a lot of information, such as consumption habits and dynamic preferences. In order to better locate user interests, this paper proposes a Bi-GRU neural network with attention to model user’s long-term historical preferences and short-term consumption motivations. First, a Bi-GRU network is established to solve the long-term dependence problem in sequences, and attention mechanism is introduced to capture user interest changes related to the target item. Then, user’s short-term interaction trajectory based on self-attention is modeled to distinguish the importance of each potential feature. Finally, combined with long-term and short-term interests, the next behavior is predicted. We conducted extensive experiments on Amazon and MovieLens datasets. The experimental results demonstrate that the proposed model outperforms current state-of-the-art models in Recall and NDCG indicators. Especially in MovieLens dataset, compared with other RNN-based models, our proposed model improved at least 2.32% at Recall@20, which verifies the effectiveness of modeling long-term and short-term interest of users, respectively.

Reducing the filter bubble effect on Twitter by considering communities for recommendations

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Quentin Grossetti ◽  
Cedric du Mouza ◽  
Nicolas Travers ◽  
Camelia Constantin
Keyword(s):  
Recommender Systems ◽  
User Engagement ◽  
User Interest ◽  
Data Set ◽  
Content Type ◽  
Proposed Model ◽  
Study Results ◽  
Filter Bubble ◽  
Filter Bubbles ◽  
The Impact

Purpose Social network platforms are considered today as a major communication mean. Their success leads to an unprecedented growth of user-generated content; therefore, finding interesting content for a given user has become a major issue. Recommender systems allow these platforms to personalize individual experience and increase user engagement by filtering messages according to user interest and/or neighborhood. Recent research results show, however, that this content personalization might increase the echo chamber effect and create filter bubbles that restrain the diversity of opinions regarding the recommended content. Design/methodology/approach The purpose of this paper is to present a thorough study of communities on a large Twitter data set that quantifies the effect of recommender systems on users’ behavior by creating filter bubbles. The authors further propose their community-aware model (CAM) that counters the impact of different recommender systems on information consumption. Findings The authors propose their CAM that counters the impact of different recommender systems on information consumption. The study results show that filter bubbles effects concern up to 10% of users and the proposed model based on the similarities between communities enhance recommendations. Originality/value The authors proposed the CAM approach, which relies on similarities between communities to re-rank lists of recommendations to weaken the filter bubble effect for these users.

Test of Mediation and Moderation between Employee Empathy and Customers’ Advocacy

2020 ◽  
Author(s):  
Sajjad Ahmad Afridi ◽  
Asad Shahjehan ◽  
Maqsood Haider ◽  
Dr Uzma Munawar
Keyword(s):  
Factor Analysis ◽  
Customer Loyalty ◽  
Service Sector ◽  
Mediating Effect ◽  
First Line ◽  
Private Hospitals ◽  
Proposed Model ◽  
Confirmatory Factor ◽  
The Impact ◽  
Significant Moderator

This study examined the impact of employee empathy on customers’ advocacy directly and indirectly through customers’ loyalty. Moreover, the interacting effect of customers’ trust was verified between the association of customers’ loyalty and advocacy. The attributes of the proposed model were examined in the context of first line employee and patients’ interactions. A total of 220 responses were collected for analysis from the private hospitals of Peshawar. The model fitness was confirmed through confirmatory factor analysis and hypotheses were examined. Findings confirmed the positive and significant impact of employee empathy on customers’ advocacy. Further, the mediating effect was examined and found that loyalty partially mediates employee empathy and customers’ advocacy. Additionally, trust was found a significant moderator between the association of customer loyalty and advocacy. Furthermore, findings revealed that trust based loyalty significantly and positively mediates employee empathy and customers’ advocacy. Findings of the present study provide understanding for the service sector, particularly in healthcare, to enhance customers’ loyalty, advocacy, and trust through service employee’s empathic aptitude. Keywords: Employee empathy, Service Eco-system, Customers’ Loyalty, Customers’ Advocacy, Trust-Based Loyalty, Healthcare, S-D Logic

The impact of duration observations on precision of results GNSS measurements

2017 ◽  
Vol 921 (3) ◽  
pp. 7-13 ◽  
Author(s):  
S.V. Grishko
Keyword(s):  
Measurement Accuracy ◽  
Functional Dependence ◽  
Observation Time ◽  
Satellite Networks ◽  
Satellite Measurements ◽  
Proposed Model ◽  
Actual Error ◽  
Gnss Measurements ◽  
The Impact ◽  
Qualitative Characteristics

This paper shows that the accuracy of relative satellite measurements depend not only on the length of the baseline, as it is regulated by the rating formula of accuracy of GNSS equipment, but also on the duration of observations. As a result of the strict adjustment much redundant satellite networks with different duration of observations obtained covariance matrix of baselines, the most realistic reflecting the actual error of satellite observations. Research of forms of communication of these errors from length of the baseline and duration of its measurement is executed. A significant influence of solar activity on accuracy of satellite measurements, in general, leads to unequal similar series of measurements made at different periods, for example, in the production of monitoring activities. The model of approximation of the functional dependence of accuracy of the baseline from its length and duration of observations having good qualitative characteristics is offered. Based on the proposed model, we analyzed the dynamics of changes in measurement accuracy with an increase in observation time.

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