Asking costs little? The impact of tasks in video QoE studies on user behavior and user ratings

2013 ◽  
Author(s):  
Andreas Sackl ◽  
Michael Seufert ◽  
Tobias Hoßfeld
Keyword(s):  
User Behavior ◽  
User Ratings ◽  
The Impact

scholarly journals Big Data Analytics for Search Engine Optimization

2020 ◽  
Vol 4 (2) ◽  
pp. 5 ◽  
Author(s):  
Ioannis C. Drivas ◽  
Damianos P. Sakas ◽  
Georgios A. Giannakopoulos ◽  
Daphne Kyriaki-Manessi
Keyword(s):  
Big Data ◽  
Cultural Heritage ◽  
Search Engine ◽  
Data Analytics ◽  
User Behavior ◽  
Big Data Analytics ◽  
Data Types ◽  
Search Engine Optimization ◽  
The Impact ◽  
The Web

In the Big Data era, search engine optimization deals with the encapsulation of datasets that are related to website performance in terms of architecture, content curation, and user behavior, with the purpose to convert them into actionable insights and improve visibility and findability on the Web. In this respect, big data analytics expands the opportunities for developing new methodological frameworks that are composed of valid, reliable, and consistent analytics that are practically useful to develop well-informed strategies for organic traffic optimization. In this paper, a novel methodology is implemented in order to increase organic search engine visits based on the impact of multiple SEO factors. In order to achieve this purpose, the authors examined 171 cultural heritage websites and their retrieved data analytics about their performance and user experience inside them. Massive amounts of Web-based collections are included and presented by cultural heritage organizations through their websites. Subsequently, users interact with these collections, producing behavioral analytics in a variety of different data types that come from multiple devices, with high velocity, in large volumes. Nevertheless, prior research efforts indicate that these massive cultural collections are difficult to browse while expressing low visibility and findability in the semantic Web era. Against this backdrop, this paper proposes the computational development of a search engine optimization (SEO) strategy that utilizes the generated big cultural data analytics and improves the visibility of cultural heritage websites. One step further, the statistical results of the study are integrated into a predictive model that is composed of two stages. First, a fuzzy cognitive mapping process is generated as an aggregated macro-level descriptive model. Secondly, a micro-level data-driven agent-based model follows up. The purpose of the model is to predict the most effective combinations of factors that achieve enhanced visibility and organic traffic on cultural heritage organizations’ websites. To this end, the study contributes to the knowledge expansion of researchers and practitioners in the big cultural analytics sector with the purpose to implement potential strategies for greater visibility and findability of cultural collections on the Web.

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 INTERNET MARKETING SOLUTIONS TRANSFORMATION DUE TO CHANGES IN CONSUMER'S PERCEPTION OF INFORMATION

2021 ◽  
Author(s):  
Igors Babics
Keyword(s):  
User Behavior ◽  
Online Communication ◽  
Marketing Research ◽  
Modern Society ◽  
Internet Marketing ◽  
Online Marketing ◽  
Scientific Article ◽  
Human Thinking ◽  
Internet User Behavior ◽  
The Impact

We have outlined the main aspects of the modern socio-economic space that have led to transformation not only in the business sector but also in human thinking. We have examined the aspects of the studied problem expressed in modern scientific works and explained the need for further study of changes in the thinking processes of consumers in the field of transformation of applied Internet marketing solutions for the requests of Internet users. We have analyzed the dynamics and trends of changes in Internet user behavior, thereby identifying the key aspects that should be taken into account when companies create online marketing strategies. We have proposed a list of steps to optimize the marketing strategy of the business in line with new realities.The relevance of the study is due to the social processes of modern society resulting in the tendency to transform consumers' thinking. COVID-19 and self-isolation have had an impact on this phenomenon, accelerating the massive changeover to online communication and online shopping.The goal of this article is to describe the results of a study of changes in consumer thinking in connection with the transformation of realities caused by the global pandemic.The scientific novelty of the study lies in highlighting the peculiarities of information perception by modern consumers associated with the global pandemic, and in substantiating the ways of transforming Internet marketing solutions for companies in an altered reality.The theoretical importance of the research lies in a better understanding of the reasons and features of the transformation of information perception by consumers in modern realities, as well as in the analysis of scientific works to study the impact of informatization and computerization on society thinking, which can be used to study this component in marketing research, including in online marketing. This is the practical value of this work.The practical value of the study lies in identifying the features of the transformation of the thinking of modern consumers through visitors to the website of Cita Lieta ltd. at ceanocosmetics.com.Like in any scientific article, this one has its research limitations. The author explores the transformation of consumer thinking change using the data from the website analytics of one company in a particular niche.

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.

Impact of bookshelf locations using eye-tracking methodology

2020 ◽  
Vol 41 (8/9) ◽  
pp. 617-629
Author(s):  
Sho Sato ◽  
Yukari Eto ◽  
Kotomi Iwaki ◽  
Tadashi Oyanagi ◽  
Yu Yasuma
Keyword(s):  
Eye Tracking ◽  
User Behavior ◽  
Public Library ◽  
Gaze Behavior ◽  
Content Type ◽  
User Behaviors ◽  
Significant Relationships ◽  
Vertical Location ◽  
The Right ◽  
The Impact

PurposeThis study aimed to understand better the user gaze behavior on bookshelves using eye-tracking technology.Design/methodology/approachAn eye-tracking experiment in a public library with 11 participants was performed. The impact of vertical shelf location of books on the number of times the books are looked at, the impact of horizontal location and the relationship between user behavior and location impact were examined by the findings.FindingsThe results showed that the vertical location of books has a significant impact on the number of times the books are looked at. More than 80% of the time spent looking at bookshelves was spent on books on the top to fourth rows. It was also revealed that the horizontal location of books has a little impact. Books located on the left side of shelves will be looked at significantly more often than those on the right side. No significant relationships between type of user behaviors and location impact were observed.Originality/valueThe study explored the impact of the vertical location of books on time spent looking at bookshelves using eye-tracking methodology. Few published studies do such experiments to address user gaze behavior on bookshelves. The study explored that the vertical location of books has a great impact, and horizontal location has a little impact on user gaze behavior.

scholarly journals The Impact of Electronic Commerce Environment on User Behavior: The Case of a Complex Product

2001 ◽  
Vol 1 (1) ◽  
pp. 41-53
Author(s):  
Jungjoo Jahng ◽  
Hemant Jain ◽  
Ramamurthy K
Keyword(s):  
Electronic Commerce ◽  
User Behavior ◽  
Complex Product ◽  
The Impact

scholarly journals Optimal Distribution Grid Operation Using DLMP-Based Pricing for Electric Vehicle Charging Infrastructure in a Smart City

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 686 ◽  
Author(s):  
Bruno Canizes ◽  
João Soares ◽  
Zita Vale ◽  
Juan Corchado
Keyword(s):  
Dynamic Pricing ◽  
Distribution System ◽  
Smart City ◽  
User Behavior ◽  
Distribution Grid ◽  
Charging Infrastructure ◽  
Energy Pricing ◽  
Electric Vehicle Charging ◽  
The Impact ◽  
Ev Charging

The use of electric vehicles (EVs) is growing in popularity each year, and as a result, considerable demand increase is expected in the distribution network (DN). Additionally, the uncertainty of EV user behavior is high, making it urgent to understand its impact on the network. Thus, this paper proposes an EV user behavior simulator, which operates in conjunction with an innovative smart distribution locational marginal pricing based on operation/reconfiguration, for the purpose of understanding the impact of the dynamic energy pricing on both sides: the grid and the user. The main goal, besides the distribution system operator (DSO) expenditure minimization, is to understand how and to what extent dynamic pricing of energy for EV charging can positively affect the operation of the smart grid and the EV charging cost. A smart city with a 13-bus DN and a high penetration of distributed energy resources is used to demonstrate the application of the proposed models. The results demonstrate that dynamic energy pricing for EV charging is an efficient approach that increases monetary savings considerably for both the DSO and EV users.

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.

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