Journal articles on the topic 'Users’ behaviors'
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Chin, Chih-Yu, Hsi-Peng Lu, and Chao-Ming Wu. "Facebook Users' Motivation for Clicking the “Like” Button." Social Behavior and Personality: an international journal 43, no. 4 (May 24, 2015): 579–92. http://dx.doi.org/10.2224/sbp.2015.43.4.579.
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To explore the motivation and behavior of Facebook users when clicking the “Like” button, we analyzed the behaviors of 743 university student Facebook users using motivational theory and the theory of reasoned action. The main study findings were as follows: (a) hedonic motivation, utilitarian motivation, compliance motivation, conformity motivation, and affiliation motivation all had a positive impact on attitudes toward “Like”-clicking behaviors; (b) subjective norms and attitudes toward “Like”-clicking behaviors all had a positive impact on behavioral intention, and (c) behavioral intention had a positive impact on actual behaviors. These findings provide a valuable basis for constructing an explanatory model for “Like”-clicking behaviors of Facebook community platform users, as well as making significant practical contributions to enhance social and commercial benefits for businesses and individuals.
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Xu, Bing, Zhijun Ding, and Hongzhong Chen. "Recommending Locations Based on Users’ Periodic Behaviors." Mobile Information Systems 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/7871502.
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The research of location recommendation system is an important topic in the field of LBSN (Location-Based Social Network). Recently, more and more researchers began focusing on researching how to recommend locations based on user’s life behavior. In this paper, we proposed a new model recommending locations based on user’s periodic behaviors. In view of multiple periodic behaviors existing in time series, an algorithm which can mine all periods in time series is proposed in this paper. Based on the periodic behaviors, we recommend locations using item-based collaborative filtering algorithm. In this paper, we will also introduce our recommendation system which can collect users’ GPS trajectory, mine user’s multiple periods, and recommend locations based user’s periodic behavior.
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Boqing Feng, Boqing Feng, Mohan Liu Boqing Feng, and Jiuqiang Jin Mohan Liu. "Density Space Clustering Algorithm Based on Users Behaviors." 電腦學刊 33, no. 2 (April 2022): 201–9. http://dx.doi.org/10.53106/199115992022043302018.
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<p>At present, insider threat detection requires a series of complex projects, and has certain limitations in practical applications; in order to reduce the complexity of the model, most studies ignore the timing of user behavior and fail to identify internal attacks that last for a period of time. In addition, companies usually categorize the behavior data generated by all users and store them in different databases. How to collaboratively process large-scale heterogeneous log files and extract characteristic data that accurately reflects user behavior is a difficult point in current research. In order to optimize the parameter selection of the DBSCAN algorithm, this paper proposes a Psychometric Data & Attack Threat Density Based Spatial Clustering of Applications with Noise algorithm (PD&AT-DBSCAN). This algorithm can improve the accuracy of clustering results. The simulation results show that this algorithm is better than the traditional DBSCAN algorithm in terms of Rand index and normalized mutual information.</p> <p> </p>
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Boqing Feng, Boqing Feng, Mohan Liu Boqing Feng, and Jiuqiang Jin Mohan Liu. "Density Space Clustering Algorithm Based on Users Behaviors." 電腦學刊 33, no. 2 (April 2022): 201–9. http://dx.doi.org/10.53106/199115992022043302018.
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<p>At present, insider threat detection requires a series of complex projects, and has certain limitations in practical applications; in order to reduce the complexity of the model, most studies ignore the timing of user behavior and fail to identify internal attacks that last for a period of time. In addition, companies usually categorize the behavior data generated by all users and store them in different databases. How to collaboratively process large-scale heterogeneous log files and extract characteristic data that accurately reflects user behavior is a difficult point in current research. In order to optimize the parameter selection of the DBSCAN algorithm, this paper proposes a Psychometric Data & Attack Threat Density Based Spatial Clustering of Applications with Noise algorithm (PD&AT-DBSCAN). This algorithm can improve the accuracy of clustering results. The simulation results show that this algorithm is better than the traditional DBSCAN algorithm in terms of Rand index and normalized mutual information.</p> <p> </p>
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Useche, Sergio A., Javier Gene-Morales, Felix W. Siebert, Francisco Alonso, and Luis Montoro. "“Not as Safe as I Believed”: Differences in Perceived and Self-Reported Cycling Behavior between Riders and Non-Riders." Sustainability 13, no. 4 (February 3, 2021): 1614. http://dx.doi.org/10.3390/su13041614.
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Cycling behavior remains a key issue for explaining several traffic causalities occurring every day. However, recent studies have shown how the assessment of the own safety-related behaviors on the road may substantially differ from how third parties assess them. Thus, the aim of this study was to evaluate the differences between cyclists’ self-reported behavior and the proxy-reported behavior that other (non-cyclist) road users perceive from bike riders. For this purpose, this study used data from two samples: (i) 1064 cyclists (M = 32.83 years) answering the Cycling Behavior Questionnaire—CBQ, and (ii) 1070 non-cyclists (M = 30.83 years) answering an adapted version of the CBQ for external raters—ECBQ. The results show how the self-reported and proxy-reported behaviors of cyclists greatly differ in terms of all behavioral factors composing the CBQ model, i.e., traffic violations, riding errors, and positive behaviors. Also, external raters (non-cyclists) are those targeting significantly riskier behaviors than those self-reported by cyclists. These discrepancies between perceived behaviors may give rise to conflicting viewpoints on the interaction between bicycle riders and other road users. Therefore, this study underscores the importance of behavioral awareness, providing highlights for future studies on the behavioral interaction between cyclists and other road users. Results can be used to improve the road safety of all road users by giving indications on self-and proxy-perceived safety-related behaviors and visibility of protective riding habits.
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Memon, Ambreen, Jeff Kilby, Jose Breñosa, Julio César Martínez Espinosa, and Imran Ashraf. "Analysis and Implementation of Human Mobility Behavior Using Similarity Analysis Based on Co-Occurrence Matrix." Sensors 22, no. 24 (December 15, 2022): 9898. http://dx.doi.org/10.3390/s22249898.
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The fast expansion of ICT (information and communications technology) has provided rich sources of data for the analysis, modeling, and interpretation of human mobility patterns. Many researchers have already introduced behavior-aware protocols for a better understanding of architecture and realistic modeling of behavioral characteristics, similarities, and aggregation of mobile users. We are introducing the similarity analytical framework for the mobile encountering analysis to allow for more direct integration between the physical world and cyber-based systems. In this research, we propose a method for finding the similarity behavior of users’ mobility patterns based on location and time. This research was conducted to develop a technique for producing co-occurrence matrices of users based on their similar behaviors to determine their encounters. Our approach, named SAA (similarity analysis approach), makes use of the device info i.e., IP (internet protocol) and MAC (media access control) address, providing an in-depth analysis of similarity behaviors on a daily basis. We analyzed the similarity distributions of users on different days of the week for different locations based on their real movements. The results show similar characteristics of users with common mobility behaviors based on location and time to showcase the efficacy. The results show that the proposed SAA approach is 33% more accurate in terms of recognizing the user’s similarity as compared to the existing similarity approach.
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Schmidt, Peter, Galit Gordoni, Icek Ajzen, Christoph Beuthner, Eldad Davidov, Henning Silber, Holger Steinmetz, and Bernd Weiß. "Twitter Users’ Privacy Behavior: A Reasoned Action Approach." Social Media + Society 8, no. 3 (July 2022): 205630512211260. http://dx.doi.org/10.1177/20563051221126085.
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Social networking sites have become a predominant means of communication across the globe. Activities on these sites generate massive amounts of personal information and raise concerns about its potential abuse. Means designed to protect the user’s privacy and prevent exploitation of confidential data often go unused. In this study, we draw on the theory of planned behavior, a reasoned action approach, to explain intentions to adopt privacy behaviors on social networking sites, with a focus on Twitter users. Consistent with the theory, an online survey of Twitter users ( n = 1,060) found that instrumental and experiential attitudes and descriptive and injunctive subjective norms regarding these behaviors were direct predictors of intentions. Perceived behavioral control had a moderating effect, such that subjective norm was a better predictor of intentions for participants high as opposed to low in perceived control. We briefly discuss the implications of these results for developing theory-driven and evidence-based interventions to promote privacy behavior.
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Herrera, Gail. "Google Scholar Users and User Behaviors: An Exploratory Study." College & Research Libraries 72, no. 4 (July 1, 2011): 316–30. http://dx.doi.org/10.5860/crl-125rl.
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The University of Mississippi Library created a profile to provide linking from Google Scholar (GS) to library resources in 2005. Although Google Scholar does not provide usage statistics for institutions, use of Google Scholar is clearly evident in looking at library link resolver logs. The purpose of this project is to examine users of Google Scholar with existing data from interlibrary loan transactions and library Web site click-through logs and analytics. Questions about user status and discipline, as well as behaviors related to use of other library resources, are explored.
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Miller, Kathleen E., Grace M. Barnes, Don Sabo, Merrill J. Melnick, and Michael P. Farrell. "A Comparison of Health Risk Behavior in Adolescent Users of Anabolic-Androgenic Steroids, by Gender and Athlete Status." Sociology of Sport Journal 19, no. 4 (December 2002): 385–402. http://dx.doi.org/10.1123/ssj.19.4.385.
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Contrary to popular assumption, adolescent anabolic-androgenic steroid use is not limited to serious male athletes. This paper examines the relationships among gender, athletic participation, and health-related problem behaviors among adolescent steroid users. Regression analyses were performed on a nationally representative sample of over 16,000 high school students (the 1997 Youth Risk Behavior Survey), of whom nearly 500 had used steroids. Compared to nonusers, steroid users were significantly more likely to report substance use, suicidal behavior, and sexual risk-taking; however, patterns of risk behavior varied by the user’s athletic status and gender. After controlling for age, race, ethnicity, and parental education, both athletic participation and female gender were negatively associated with most risk behaviors among users of anabolic steroids.
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Mu, Xiaodong, Zhaoju Zeng, Danyao Shen, and Bo Zhang. "Multi-Feature Behavior Relationship for Multi-Behavior Recommendation." Applied Sciences 12, no. 24 (December 15, 2022): 12909. http://dx.doi.org/10.3390/app122412909.
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Multi-behavior recommendation aims to model the interaction information of multiple behaviors to enhance the target behavior’s recommendation performance. Despite progress in recent research, it is challenging to represent users’ preferences using the multi-feature behavior information of user interactions. In this paper, we propose a Multi-Feature Behavior Relationship for Multi-Behavior Recommendation (MFBR) framework, which models the multi-behavior recommendation problem from both sequence structure and graph structure perspectives for user preference prediction of target behaviors. Specifically, the MFBR model is designed with a sequence encoder and a graph encoder to construct behavioral representations of different aspects of the user; the correlations between behaviors are modeled by a behavioral relationship encoding layer, and the importance of different behaviors is finally learned in order to construct the final representation of user preferences. Experimental validation conducted on two real-world recommendation datasets shows that our MFBR consistently outperforms state-of-the-art methods.
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Das, Amit, and Habib Ullah Khan. "Security behaviors of smartphone users." Information & Computer Security 24, no. 1 (March 14, 2016): 116–34. http://dx.doi.org/10.1108/ics-04-2015-0018.
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Purpose – This paper aims to report on the information security behaviors of smartphone users in an affluent economy of the Middle East. Design/methodology/approach – A model based on prior research, synthesized from a thorough literature review, is tested using survey data from 500 smartphone users representing three major mobile operating systems. Findings – The overall level of security behaviors is low. Regression coefficients indicate that the efficacy of security measures and the cost of adopting them are the main factors influencing smartphone security behaviors. At present, smartphone users are more worried about malware and data leakage than targeted information theft. Research limitations/implications – Threats and counter-measures co-evolve over time, and our findings, which describe the state of smartphone security at the current time, will need to be updated in the future. Practical implications – Measures to improve security practices of smartphone users are needed urgently. The findings indicate that such measures should be broadly effective and relatively costless for users to implement. Social implications – Personal smartphones are joining enterprise networks through the acceptance of Bring-Your-Own-Device computing. Users’ laxity about smartphone security thus puts organizations at risk. Originality/value – The paper highlights the key factors influencing smartphone security and compares the situation for the three leading operating systems in the smartphone market.
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Wahyuni, Tri. "The Influence of Technology Acceptance Model (TAM) on The Users’ Behavior of Sikesya Application in IAIN Surakarta." Shirkah: Journal of Economics and Business 1, no. 1 (April 30, 2016): 47. http://dx.doi.org/10.22515/shirkah.v1i1.15.
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This study aims to evaluate the student acceptance of SIKESYA (Sharia Financial System/Sikesya) application as the users by using the framework of Technology Acceptance Model (TAM) and its development. The constructs being tested in this research are perceived usefulness, perceived ease of use, experience, social influence, attitute toward behavior, behavioral intention, facilitating condition, and user behaviors. As much as 80 students has been chosen as sample which were determined using purposive sampling method. The data gathered was then analyzed using partial least square (PLS). The result showed that experience did not influence the perceived ease of use, on the other hand perceived usefulness has a positif influence toward users attitude and behavior in using Sikesya, while the perceived ease of use did not influence the users atttitude and behavior at all, since the students would still use it as it is an application used as part of university services. The attitude and behavior did not influence the behavioral intention, whereas the social influence has a positif effect on behavioral intention, yet the behavioral intention gave positif impact to user’s behavior. On the other hand, facilitating condition has no effect toward users’ behavior. Keywords: Sharia Financial System (SIKESYA), Technology Acceptance Model (TAM), IAIN Surakarta
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Zhao, Guoshuai, Xueming Qian, and Xing Xie. "User-Service Rating Prediction by Exploring Social Users' Rating Behaviors." IEEE Transactions on Multimedia 18, no. 3 (March 2016): 496–506. http://dx.doi.org/10.1109/tmm.2016.2515362.
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Gamage, Dilrukshi, Indika Perera, and Shantha Fernando. "Exploring MOOC User Behaviors Beyond Platforms." International Journal of Emerging Technologies in Learning (iJET) 15, no. 08 (April 24, 2020): 161. http://dx.doi.org/10.3991/ijet.v15i08.12493.
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MOOC user behavior is generally studied using the data collected within platform interactions in the learning system or via outside social media platforms. It is important to understand the root causes of anomalies in MOOCs, such as the 80% attrition, less interactions within platforms and what causing the reflected behaviors beyond platforms. We study MOOC student behaviors outside the platform using ethnographic methods, mainly focusing on diary study and interviews. Two groups, 11 extreme users who have completed many MOOCs and 10 who never completed MOOC have been used to collect data. The log sheets data and interviews were analyzed using the Epistemic Network Analysis (ENA) method to explore if there is a significance between these 2 groups and other qualitative comparisons to explore behavioral patterns. Our results indicated 4 behavioral patterns with insights into a significant level of learner's habits between extreme and novice users’ behaviors leading to completion or dropping. This reflects the design gaps of MOOC platforms and based on the behavioral patterns, we provide recommendations to meet the learners' needs.
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Kouabenan, Dongo Rémi, Mihaela Calatan, Marc Gandit, and Sandrine Caroly. "Behaviors and Causal Explanations of Road-Tunnel Users During a Fire." Psihologia Resurselor Umane 9, no. 1 (January 24, 2020): 69–84. http://dx.doi.org/10.24837/pru.v9i1.394.
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The present study was aimed at describing the behaviors of tunnel users in the event of a road-tunnel fire,and to determine the effect of stress on these behaviors. Another aim was to identify the causal explanationsoffered by tunnel users for fires and for non-evacuation behaviors after a fire alarm is given. Several fire scenarioswere presented to 217 participants, who were asked to predict their likely behavior in the situations described,and to give explanations for the fire's occurrence. The participants' perceived stress level was also measured usinga subscale taken from the Depression Anxiety Stress Scales (DASS; Lovibond, & Lovibond, 1995). The resultsshowed that the participants tended to adopt more risky behaviors in situations where traffic was moving freelythan in congested traffic. The users' perceived stress led them to adopt unsafe behaviors, but contrary to Hennessyand Wiesenthal's (1997) results, this relationship was stronger in free-flowing traffic than in a traffic jam. Someof the participants demonstrated a certain behavioral rigidity, tending to adopt identical behaviors regardless ofthe traffic situation. The behaviors stated for a given situation seem to be consistent, but they were not alwayssafety-conscious. And the more serious the fire, the more internal the explanations were. Finally, non-evacuationbehaviors were attributed mainly to internal factors that implicated the concerned individuals. Some suggestionsfor long-term preventive actions based on users' beliefs and representations are proposed
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Luo, Mingshi, Xiaoli Zhang, Jiao Li, Peipei Duan, and Shengnan Lu. "User Dynamic Preference Construction Method Based on Behavior Sequence." Scientific Programming 2022 (July 22, 2022): 1–15. http://dx.doi.org/10.1155/2022/6101045.
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People’s needs are constantly changing, and the performance of traditional recommendation algorithms is no longer enough to meet the demand. Considering that users’ preferences change with time, the users’ behavior sequence hides the evolution and change law of users’ preferences, so mining the dependence of the users’ behavior sequence is extremely important to predict users’ dynamic preferences. From the perspective of constructing users’ dynamic preferences, this paper proposes a users’ dynamic preference model based on users’ behavior sequences. Firstly, the user’s interest model is divided into short-term and long-term interest models. The short-term interest reflects the user’s current preference, and the long-term interest refers to the user’s interest from all his historical behaviors, representing the user’s consistent and stable preference. Users’ dynamic preference is obtained by integrating short-term interest and long-term interest, which solves the problem that the user’s preference cannot reflect the change in the user’s interest in real-time. We use the public Amazon review dataset to test the model we propose in the paper. Our model achieves the best performance, with a maximum performance improvement of 15.21% compared with the basic model (BPR, NCF) and 2.04% compared with the sequence model (GRU4REC, Caser, etc.), which proves that the user’s dynamic preference model can effectively predict the user’s dynamic preference. Users’ dynamic preferences are helpful in predicting users’ real-time preferences, especially in the field of recommendation.
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Hu, Changhong, Shufen Liu, Ramana Reddy, Sumitra Reddy, and Mingyang Liu. "KaM_CRK: Clustering and Ranking Knowledge for Reasonable Results Based on Behaviors and Contexts." Mathematical Problems in Engineering 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/601528.
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A model named KaM_CRK is proposed, which can supply the clustered and ranked knowledge to the users on different contexts. By comparing the attributes of contexts and JANs, our findings indicate that our model can accumulate the JANs, whose attributes are similar with the user’s contexts, together. By applying the KaM_CLU algorithm and Centre rank strategy into the KaM_CRK model, the model boosts a significant promotion on the accuracy of provision of user's knowledge. By analyzing the users' behaviors, the dynamic coefficient BehaviorFis first presented in KaM_CLU. Compared to traditional approaches of K_means and DBSCAN, the KaM_CLU algorithm does not need to initialize the number of clusters. Additionally, its synthetic results are more accurate, reasonable, and fit than other approaches for users. It is known from our evaluation through real data that our strategy performs better on time efficiency and user's satisfaction, which will save by 30% and promote by 5%, respectively.
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Sylvester, F. Ley. "Mobile Device Users’ Susceptibility to Phishing Attacks." International Journal of Computer Science and Information Technology 14, no. 1 (February 28, 2022): 1–18. http://dx.doi.org/10.5121/ijcsit.2022.14101.
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The mobile device is one of the fasted growing technologies that is widely used in a diversifying sector. Mobile devices are used for everyday life, such as personal information exchange – chatting, email, shopping, and mobile banking, contributing to information security threats. Users' behavior can influence information security threats. More research is needed to understand users' threat avoidance behavior and motivation. Using Technology threat avoidance theory (TTAT), this study assessed factors that influenced mobile device users' threat avoidance motivations and behaviors as it relates to phishing attacks. From the data collected from 137 mobile device users using a questionnaire, the findings indicate that (1) mobile device users' perceived susceptibility and severity of phishing attacks have a significant correlation with a users' perception of the threat; (2) mobile device users' motivation to avoid a threat is correlated to a users' behavior in avoiding threat; and (3) a mobile device user's susceptibility to phishing attacks can be reduced by their perception of the threat. These findings reveal that a user's perception of threat increases if they perceive that the consequence of such threat to their mobile devices will be severe, thereby increasing a user's motivation and behavior to avoid phishing attack threats. This study is beneficial to mobile device users in personal and organizational settings.
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He, Jie. "Construction of Internet TV Industry Ecosystem Based on Data Mining Technology." Wireless Communications and Mobile Computing 2022 (March 3, 2022): 1–9. http://dx.doi.org/10.1155/2022/3719372.
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While the Internet provides people with convenience, it also comes with security concerns. Users can more easily form groups, distort facts, and contribute to some sensitive topics on the internet. As a result, identifying and analyzing users’ online behaviors are critical. This paper creates a new Internet TV industry ecosystem using DM (data mining) technology. The recommendation system model is established based on data from users’ on-demand viewing behavior across the entire network, and the functions of various system modules and their coordination ability are described in detail. The evolution of users’ online time is examined, providing data to support and explain the prediction analysis of users’ click behavior and the analysis of users’ search intent. The type of web page that the user clicks on can reveal the user’s behavior tendency.
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Zhang, Pei Ying, Ya Jun Du, and Chang Wang. "Clustering Users According to Common Interest Based on User Search Behavior." Advanced Materials Research 143-144 (October 2010): 851–55. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.851.
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The paper presents a novel method to cluster users who share the common interest and discover their common interest domain by mining different users’ search behaviors in the user session, mainly the consecutive search behavior and the click sequence considering the click order and the syntactic similarity. The community is generated and this information will be used in the recommendation system in the future. Also the method is ‘content-ignorant’ to avoid the storage and manipulation of a large amount of data when clustering the web pages by content. The experiment proved it an available and effective way.
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Shi, Juanjuan. "Music Recommendation Algorithm Based on Multidimensional Time-Series Model Analysis." Complexity 2021 (April 27, 2021): 1–11. http://dx.doi.org/10.1155/2021/5579086.
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This paper proposes a personalized music recommendation method based on multidimensional time-series analysis, which can improve the effect of music recommendation by using user’s midterm behavior reasonably. This method uses the theme model to express each song as the probability of belonging to several hidden themes, then models the user’s behavior as multidimensional time series, and analyzes the series so as to better predict the use of music users’ behavior preference and give reasonable recommendations. Then, a music recommendation method is proposed, which integrates the long-term, medium-term, and real-time behaviors of users and considers the dynamic adjustment of the influence weight of the three behaviors so as to further improve the effect of music recommendation by adopting the advanced long short time memory (LSTM) technology. Through the implementation of the prototype system, the feasibility of the proposed method is preliminarily verified.
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Youn, Bangbu, Seungwan Hong, and DaeHyun Kim. "Health Behaviors in Combustible Cigarette, Heated Tobacco Users and Quitters." Keimyung Medical Journal 41, no. 2 (December 15, 2022): 92–96. http://dx.doi.org/10.46308/kmj.2022.00157.
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Smoking can be changed by health behavior education on the fact that unhealthy behaviors can cause chronic diseases and cancer and that it is important to identify health behaviors in smoking. We try to compare the health behaviors of combustible cigarette (CC), heated tobacco (HT) users, and quitters. Smoking behaviors were divided into three groups (CC, HT users, and quitters). The HT user group (n = 100) was selected among those who underwent a health examination in 2021-2022. CC smokers cohort group (n = 100) and quitters cohort (n = 100) were randomly selected from the same groups (age ± 2) who underwent a health examination in the same period. Sleep-related problems (snoring and sleep apnea), alcohol consumption, and exercise were compared in the CC group, HT group, and quitters group, respectively. Snoring was more common in the quitters group (27%) than in the CC users group (19%) and HT users group (18%). It can be related to weight gain during quitting tobacco use. Nondrinkers were more common in the CC users group (21%) than the HT users group (8%) and quitters group (10%). CC users seem to be more concerned about the health effects of drinking compared to HT users and quitters. Anaerobic exercise was different among groups, and aerobic exercise was not. HT users group did more aerobic exercise than CC users and quitters group. Differences in healthy behaviors among CC and HC users and quitters can be useful information for health education to smokers. Understanding smokers’ health behavior is important to smoking cessation counseling in clinical practice.
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Burigat, S., L. Chittaro, and L. Ieronutti. "Mobrex: Visualizing Users' Mobile Browsing Behaviors." IEEE Computer Graphics and Applications 28, no. 1 (January 2008): 24–32. http://dx.doi.org/10.1109/mcg.2008.13.
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Iglesias, Jose Antonio, Agapito Ledezma, and Araceli Sanchis. "Evolving classification of UNIX users’ behaviors." Evolving Systems 5, no. 4 (February 21, 2014): 231–38. http://dx.doi.org/10.1007/s12530-014-9104-2.
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Yan, Huan, Zifeng Wang, Tzu-Heng Lin, Yong Li, and Depeng Jin. "Profiling users by online shopping behaviors." Multimedia Tools and Applications 77, no. 17 (December 11, 2017): 21935–45. http://dx.doi.org/10.1007/s11042-017-5365-7.
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Hogg, Tad, and Gabor Szabo. "Diversity of User Activity and Content Quality in Online Communities." Proceedings of the International AAAI Conference on Web and Social Media 3, no. 1 (March 19, 2009): 58–65. http://dx.doi.org/10.1609/icwsm.v3i1.13940.
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Web sites where users create and rate content display long-tailed distributions in many aspects of behavior. Using one such community site, Essembly, we propose and evaluate mechanisms to explain these behaviors. Unlike purely descriptive models, these mechanisms rely on user behaviors based on information available to each user. For Essembly, we find the long-tails arise from large differences among user activity rates, the time users devote to the site, and qualities of the rated content. The models not only explain overall behavior but also allow estimating the properties of users and content from their early behaviors.
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Hwang, Youjin, Hyung Jun Kim, Hyung Jin Choi, and Joonhwan Lee. "Exploring Abnormal Behavior Patterns of Online Users With Emotional Eating Behavior: Topic Modeling Study." Journal of Medical Internet Research 22, no. 3 (March 31, 2020): e15700. http://dx.doi.org/10.2196/15700.
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Background Emotional eating (EE) is one of the most significant symptoms of various eating disorders. It has been difficult to collect a large amount of behavioral data on EE; therefore, only partial studies of this symptom have been conducted. To provide adequate support for online social media users with symptoms of EE, we must understand their behavior patterns to design a sophisticated personalized support system (PSS). Objective This study aimed to analyze the behavior patterns of emotional eaters as the first step to designing a personalized intervention system. Methods The machine learning (ML) framework and Latent Dirichlet Allocation (LDA) topic modeling tool were used to collect and analyze behavioral data on EE. Data from a subcommunity of Reddit, /r/loseit, were analyzed. This dataset included all posts and feedback from July 2014 to May 2018, comprising 185,950 posts and 3,528,107 comments. In addition, deleted and improperly collected data were eliminated. Stochastic gradient descent–based ML classifier with an accuracy of 90.64% was developed to collect refined behavioral data of online users with EE behaviors. The expert group that labeled the dataset to train the ML classifiers included a medical doctor specializing in EE diagnosis and a nutritionist with profound knowledge of EE behavior. The experts labeled 5126 posts as EE (coded as 1) or others (coded as 0). Finally, the topic modeling process was conducted with LDA. Results The following 4 macroperspective topics of online EE behaviors were identified through linguistic evidence regarding each topic: addressing feelings, sharing physical changes, sharing and asking for dietary information, and sharing dietary strategies. The 5 main topics of feedback were dietary information, compliments, consolation, automatic bot feedback, and health information. The feedback topic distribution significantly differed depending on the type of EE behavior (overall P<.001). Conclusions This study introduces a data-driven approach for analyzing behavior patterns of social website users with EE behaviors. We discovered the possibility of the LDA topic model as an exploratory user study method for abnormal behaviors in medical research. We also investigated the possibilities of ML- and topic modeling–based classifiers to automatically categorize text-based behavioral data, which could be applied to personalized medicine in future research.
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Wu, Dan, Rui Qiao, and Yi Li. "A study on location-based mobile map search behavior." Program 50, no. 3 (July 4, 2016): 246–69. http://dx.doi.org/10.1108/prog-11-2015-0074.
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Purpose – Mobile users increasingly employ location-based map searches in their daily lives. However, it is still relatively unknown about mobile users’ map related search behaviors. The purpose of this paper is to discover the interactions between the users and mobile map search systems, to reveal the shortcomings of existing mobile map search functions, and to propose improvement suggestions. Design/methodology/approach – Based on a set of controlled user experiments performed on the Baidu mobile phone map, this paper empirically examines users’ location-based mobile search behaviors, such as timing, metering, judging and so on. This paper also conducts statistical correlation tests to generate relation tables and diagrams regarding each variable, for example, the relation between the retrieval time and the retrieval steps. Findings – The results indicate that mobile map users have two important characteristics in their search behaviors: first, mobile map users always follow the single search path. Second, the mobile map search efficiency of users is always low. Research limitations/implications – The situation simulation testing method is mainly used for the construction of a mobile information search behavior environment, which may make the users be nervous and have some effect on the search efficiency. Practical implications – Based on the identification of user behaviors, this paper provides suggestions to optimize and improve mobile map search systems. Originality/value – This paper studies users’ mobile map search behavior based on location and explores the features of user behavior from the perspective of human-computer interaction.
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Liang, Shaobo, Dan Wu, and Jing Dong. "Understanding the Paths and Patterns of App-Switching Experiences in Mobile Searches." Sustainability 14, no. 20 (October 11, 2022): 12992. http://dx.doi.org/10.3390/su142012992.
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Mobile searches have become the main channel for people to search for information, and mobile searches have received attention in the field of information-seeking behavior. Especially as users use various apps to search network information, the app-switching behaviors in mobile searches have also attracted scholars’ attention in recent years. Research on app-switching behaviors in mobile searches can help to further understand users’ search motivations, evaluate search results, and improve users’ mobile search experiences. This study recruited participants (n = 30) and conducted a 15-day user experiment. This study collected all participants’ mobile phone log data during the experiment and identified the app-switching behaviors in mobile searches through a log collection tool. This study aimed to discover the app-switching behavior paths and patterns in mobile searches. Firstly, the basic characteristics of app-switching behaviors in mobile searches were analyzed, as were the app-switching paths in mobile searches from the perspective of switching probability between apps. Then, the different behaviors in mobile search sessions were identified and app-switching behavior patterns were put forward. These behavior patterns summarize user behavior changes in mobile search sessions. This paper focused on analyzing app-switching behavior paths under different patterns and found apparent differences in app-switching behavior paths. This study examined mobile search behavior from the perspective of app-switching. The research of this paper can help to better understand the relationship between users’ mobile search behaviors and app interactions and is an excellent supplement to the analysis of mobile search behaviors.
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Li, Pengfei, Yin Zhang, and Bin Zhang. "Understanding Query Combination Behavior in Exploratory Searches." Applied Sciences 12, no. 2 (January 11, 2022): 706. http://dx.doi.org/10.3390/app12020706.
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In exploratory search, users sometimes combine two or more issued queries into new queries. We present such a kind of search behavior as query combination behavior. We find that the queries after combination usually can better meet users’ information needs. We also observe that users combine queries for different motivations, which leads to different types of query combination behaviors. Previous work on understanding user exploratory search behaviors has focused on how people reformulate queries, but not on how and why they combine queries. Being able to answer these questions is important for exploring how users search and learn during information retrieval processes and further developing support to assist searchers. In this paper, we first describe a two-layer hierarchical structure for understanding the space of query combination behavior types. We manually classify query combination behavior sessions from AOL and Sogou search engines and explain the relationship from combining queries to success. We then characterize some key aspects of this behavior and propose a classifier that can automatically classify types of query combination behavior using behavioral features. Finally, we summarize our findings and show how search engines can better assist searchers.
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Laor, Tal, and Yair Galily. "In WAZE we trust? GPS-based navigation application users’ behavior and patterns of dependency." PLOS ONE 17, no. 11 (November 10, 2022): e0276449. http://dx.doi.org/10.1371/journal.pone.0276449.
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Functional technological applications have become an integral part of our lives changing our patterns of reasoning and behavior. The current study examines whether, how and why use of WAZE app, a popular GPS-based navigation application, demonstrate behaviors and patterns which resemble those of technological dependency. We conducted semi-structured in-depth interviews with 50 WAZE users. The questions took inspiration from the model of IT addiction, which identifies six behavioral parameters: withdrawal, conflict, mood modification, relapse, tolerance, and saliency. The novelty of the study lies in the evidence of patterns and behaviors which resemble technological dependency on the WAZE app. The findings indicate that WAZE app satisfies users’ needs driven by functionality. Four behavioral characteristics associated with IT addiction are applicable to WAZE users: mood modification, conflict, relapse, and withdrawal. The study concludes that functional technological applications may trigger behavioral indicators of technological addiction.
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Liu, Xiaoping, and Hong He. "How do CSR disclosures facilitate knowledge-sharing behaviors?" Marketing Intelligence & Planning 40, no. 3 (February 1, 2022): 328–43. http://dx.doi.org/10.1108/mip-10-2021-0368.
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PurposeDrawing on the stakeholder theory and stimulus-organism-response (S-O-R) model, this study examines the relationship between corporate social responsibility (CSR) disclosures and users' knowledge-sharing behaviors on social media (SM). Two underlying mechanisms are used to explain the relationship between CSR disclosures and knowledge sharing, namely, CSR identification and content richness.Design/methodology/approachAn empirical analysis based on a negative binomial regression model is conducted using CSR data disclosed on corporate official Microblog in the past year on 30 companies with a high CSR development index in China.FindingsCSR disclosures are positively related to users' knowledge-sharing behaviors, and this relationship is mediated by CSR identification. Content richness strengthens the positive relationship between CSR disclosures and users' CSR identification. User's retweeting behavior is positively related to commenting behavior.Originality/valueThis is one of the few studies to investigate the relationship between CSR disclosures and knowledge sharing on SM. The findings of this study can help companies formulate and implement effective CSR disclosure strategies to achieve sustainable development of companies.
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Castro, Felipe G., Michael D. Newcomb, and Karen Cadish. "Lifestyle Differences between Young Adult Cocaine Users and Their Nonuser Peers." Journal of Drug Education 17, no. 2 (June 1987): 89–111. http://dx.doi.org/10.2190/878h-u394-ukn3-dvd5.
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Health-related behaviors were examined in a group of twenty-five young adults who regularly used cocaine and in a matched sample of twenty-five nonusing young adults. We hypothesized that cocaine users would have a less healthy lifestyle as indicated by behavioral scales or items on three health domains: Nonillicit Drugs, Health Orientation, and Health Behaviors. Cocaine users consumed more cups of coffee per day, more alcoholic beverages per week, and ate fewer complete/balanced meals per day than non-users. Cocaine users also reported fewer relaxing or stress-reducing activities and less daily planning and organization. A within-groups analysis of the cocaine users revealed that the heavier users perceived themselves as less healthy relative to their peers and ate fewer complete/balanced meals. These results suggest that cocaine use is a behavior embedded within a complex of interrelated unhealthy behaviors that constitute an unhealthy lifestyle. By implication, cocaine use is associated with a greater lifestyle imbalance involving polydrug use at the expense of nutrition and effective self-management. These results suggest that clinical interventions for prevention and treatment of cocaine use should promote specific healthy lifestyle changes in addition to the current practice of promoting a cessation of drug use.
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Cassola, Fernando, Leonel Morgado, António Coelho, Hugo Paredes, António Barbosa, Helga Tavares, and Filipe Soares. "Using Virtual Choreographies to Identify Office Users’ Behaviors to Target Behavior Change Based on Their Potential to Impact Energy Consumption." Energies 15, no. 12 (June 14, 2022): 4354. http://dx.doi.org/10.3390/en15124354.
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Reducing office buildings’ energy consumption can contribute significantly towards carbon reduction commitments since it represents ∼40% of total energy consumption. Major components of this are lighting, electrical equipment, heating, and central cooling systems. Solid evidence demonstrates that individual occupants’ behaviors impact these energy consumption components. In this work, we propose the methodology of using virtual choreographies to identify and prioritize behavior-change interventions for office users based on the potential impact of specific behaviors on energy consumption. We studied the energy-related office behaviors of individuals by combining three sources of data: direct observations, electricity meters, and computer logs. Data show that there are behaviors with significant consumption impact but with little potential for behavioral change, while other behaviors have substantial potential for lowering energy consumption via behavioral change.
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Alohali, Manal, Nathan Clarke, Fudong Li, and Steven Furnell. "Identifying and predicting the factors affecting end-users’ risk-taking behavior." Information & Computer Security 26, no. 3 (July 9, 2018): 306–26. http://dx.doi.org/10.1108/ics-03-2018-0037.
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Purpose The end-user has frequently been identified as the weakest link; however, motivated by the fact that different users react differently to the same stimuli, identifying the reasons behind variations in security behavior and why certain users could be “at risk” more than others is a step toward protecting and defending users against security attacks. This paper aims to explore the effect of personality trait variations (through the Big Five Inventory [BFI]) on users’ risk level of their intended security behaviors. In addition, age, gender, service usage and information technology (IT) proficiency are analyzed to identify what role and impact they have on behavior. Design/methodology/approach The authors developed a quantitative-oriented survey that was implemented online. The bi-variate Pearson two-tailed correlation was used to analyze survey responses. Findings The results obtained by analyzing 538 survey responses suggest that personality traits do play a significant role in affecting users’ security behavior risk levels. Furthermore, the results suggest that BFI score of a trait has a significant effect as users’ online personality is linked to their offline personality, especially in the conscientiousness personality trait. Additionally, this effect was stronger when personality was correlated with the factors of IT proficiency, gender, age and online activity. Originality/value The contributions of this paper are two-fold. First, with the aid of a large population sample, end-users’ security practice is assessed from multiple domains, and relationships were found between end-users’ risk-taking behavior and nine user-centric factors. Second, based upon these findings, the predictive ability for these user-centric factors were evaluated to determine the level of risk a user is subject to from an individual behavior perspective. Of 28 behaviors, 11 were found to have a 60 per cent or greater predictive ability, with the highest classification of 92 per cent for several behaviors. This provides a basis for organizations to use behavioral intent alongside personality traits and demographics to understand and, therefore, manage the human aspects of risk.
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Alonso, Francisco, Cristina Esteban, Mireia Faus, and Sergio A. Useche. "Differences in the Assessment of Safe and Risky Driving Behaviors: Pedestrians Versus Drivers." SAGE Open 12, no. 2 (April 2022): 215824402211024. http://dx.doi.org/10.1177/21582440221102444.
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Interactions between pedestrians and drivers are an important traffic safety issue. Psycho-social factors such as thoughts, perceptions and attitudes toward other people can be reliable predictors of riskier or safer behaviors among road users. The aim of this study was to assess how frequently participants perceive that drivers perform safe and risky road behaviors through drivers’ self-reported behavior and pedestrians and other drivers’ external perceptions. The results show that pedestrians assess the road behaviors of drivers in a seriously negative way. Meanwhile, drivers perceive their own behaviors as more appropriate than those performed by the rest of drivers. Women attribute more favorable assessments to other users’ road behavior. Similarly, older drivers do the same, and consider themselves “safer” users. On the contrary, younger drivers report a higher frequency of self-rated unsafe behaviors. The study highlights the importance of working on the awareness of self-rated road behaviors. Road safety interventions and programs in Spain must consider the differences related to gender and age.
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Ondia, Eric Prince, Sirimas Hengrasmee, and Sant Chansomsak. "Spatial Configuration and Users’ Behavior in Co-Working Spaces." YBL Journal of Built Environment 6, no. 1 (April 17, 2018): 20–36. http://dx.doi.org/10.2478/jbe-2018-0002.
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Abstract This paper aims to examine whether there is a direct relationship between spatial configuration and users’ behaviors in co-working spaces, and if so, how this environment and behavior relationship impacts their working process. The study employed ethnographic qualitative strategy as the general method of inquiry and used visual documentation, direct observations, and behavioral mapping as methods of data collection in two case studies. Analysis of the findings demonstrates that design elements such as barriers and fields are powerful tools for influencing and guiding users’ behavior within coworking spaces. The findings provide a deeper understanding of the relationship between design and behavioral patterns in co-working spaces. The research insights in this study may inform architects, policymakers and facility managers in making conscious decisions on the design of co-working spaces that are more meaningful to the users.
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Zhao, Zhongying, Hui Zhou, Bijun Zhang, Fujiao Ji, and Chao Li. "Identifying High Influential Users in Social Media by Analyzing Users’ Behaviors." Journal of Intelligent & Fuzzy Systems 36, no. 6 (June 11, 2019): 6207–18. http://dx.doi.org/10.3233/jifs-182512.
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Guan, Dejun. "Mobile Learning Platform in Cloud Computing with Information Security and Android System." Security and Communication Networks 2022 (February 21, 2022): 1–8. http://dx.doi.org/10.1155/2022/5491411.
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In order to meet the real-time communication between teachers and students and improve students' interest and efficiency in learning, this paper designs a mobile learning communication system based on information security and Android system with cloud computing. By using the method of combining database records and web logs to mine the user's browsing records and behaviors, these implicit user behaviors are transformed into explicit user evaluations of the project. Then, the cosine similarity calculation method is used to calculate users and the similarity between the users. The users are clustered by the K-means clustering method, so that the users are automatically divided into several user clusters according to their behaviors. Finally, the user's nearest neighbor score is used to predict the pair. Based on the above method, a mobile learning communication system based on Android is realized, and the system basically meets the functional needs of users. The development of this system not only promotes the mutual communication between students but also facilitates the students' learning, which has a certain promoting effect on the improvement of their academic performance.
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Li, Zhengren, Xiaohang Zhang, Yanyu Wang, and Xin Su. "Predicting the sequential behavior of mobile Internet users based on MSM model." International Journal of Market Research 62, no. 6 (August 13, 2019): 743–57. http://dx.doi.org/10.1177/1470785319870161.
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The behavior of users concerning mobile Internet varies significantly throughout the day. Results from existing studies—which generally simply segment one day into morning, afternoon, and evening—often provide inaccurate predictions of the behavior of users. To improve prediction accuracy, we propose a segment-based multi-state Markov (SBMSM) model for the dynamic time interval segmentation of the sequential behavior of users. The specific procedure of this proposed model can be described as follows: first, we divide each user’s behaviors into minimum unit according to time dimension; then, we merge adjacent time intervals or ensure they are constant according to the similarities in behavior; and finally, a multi-state Markov (MSM) model is trained using the newly constructed data individually. The experimental results illustrate that for 95.78% of users, an SBMSM model performs much better than a naive MSM model and hidden Markov model.
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Ouyang, Yi, Bin Guo, Xing Tang, Xiuqiang He, Jian Xiong, and Zhiwen Yu. "Mobile App Cross-Domain Recommendation with Multi-Graph Neural Network." ACM Transactions on Knowledge Discovery from Data 15, no. 4 (June 2021): 1–21. http://dx.doi.org/10.1145/3442201.
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With the rapid development of mobile app ecosystem, mobile apps have grown greatly popular. The explosive growth of apps makes it difficult for users to find apps that meet their interests. Therefore, it is necessary to recommend user with a personalized set of apps. However, one of the challenges is data sparsity, as users’ historical behavior data are usually insufficient. In fact, user’s behaviors from different domains in app store regarding the same apps are usually relevant. Therefore, we can alleviate the sparsity using complementary information from correlated domains. It is intuitive to model users’ behaviors using graph, and graph neural networks have shown the great power for representation learning. In this article, we propose a novel model, Deep Multi-Graph Embedding (DMGE), to learn cross-domain app embedding. Specifically, we first construct a multi-graph based on users’ behaviors from different domains, and then propose a multi-graph neural network to learn cross-domain app embedding. Particularly, we present an adaptive method to balance the weight of each domain and efficiently train the model. Finally, we achieve cross-domain app recommendation based on the learned app embedding. Extensive experiments on real-world datasets show that DMGE outperforms other state-of-art embedding methods.
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Xiong, Wei, Michael Recce, and Brook Wu. "Intent-Based User Segmentation with Query Enhancement." International Journal of Information Retrieval Research 3, no. 4 (October 2013): 1–17. http://dx.doi.org/10.4018/ijirr.2013100101.
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With the rapid advancement of the internet, accurate prediction of user's online intent underlying their search queries has received increasing attention from online advertising community. This paper aims to address the major challenges with user queries in the context of behavioral targeting advertising by proposing a query enhancement mechanism that augments user's queries by leveraging a user query log. The empirical evaluation demonstrates that the authors' methodology for query enhancement achieves greater improvement than the baseline models in both intent-based user classification and user segmentation. Different from traditional user segmentation methods, which take little semantics of user behaviors into consideration, the authors propose a novel user segmentation strategy by incorporating the query enhancement mechanism with a topic model to mine the relationships between users and their behaviors in order to segment users in a semantic manner. Comparing with a classical clustering algorithm, K-means, the experimental results indicate that the proposed user segmentation strategy helps improve behavioral targeting effectiveness significantly. This paper also proposes an alternative to define user's search intent for the evaluation purpose, in the case that the dataset is sanitized. This approach automatically labels users in a click graph, which are then used in training an intent-based user classifier.
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Ren, Xinyu, Seyyed Mohammadreza Rahimi, and Xin Wang. "Utilization of Real Time Behavior and Geographical Attraction for Location Recommendation." ACM Transactions on Spatial Algorithms and Systems 8, no. 1 (March 31, 2022): 1–30. http://dx.doi.org/10.1145/3484318.
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Personalized location recommendation is an increasingly active topic in recent years, which recommends appropriate locations to users based on their temporal and geospatial visiting patterns. Current location recommendation methods usually estimate the users’ visiting preference probabilities from the historical check-ins in batch. However, in practice, when users’ behaviors are updated in real-time, it is often cost-inhibitive to re-estimate and updates users’ visiting preference using the same batch methods due to the number of check-ins. Moreover, an important nature of users’ movement patterns is that users are more attracted to an area where have dense locations with same categories for conducting specific behaviors. In this paper, we propose a location recommendation method called GeoRTGA by utilizing the real time user behaviors and geographical attractions to tackle the problems. GeoRTGA contains two sub-models: real time behavior recommendation model and attraction-based spatial model. The real time behavior recommendation model aims to recommend real-time possible behaviors which users prefer to visit, and the attraction-based spatial model is built to discover the category-based spatial and individualized spatial patterns based on the geographical information of locations and corresponding location categories and check-in numbers. Experiments are conducted on four public real-world check-in datasets, which show that the proposed GeoRTGA outperforms the five existing location recommendation methods.
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Khanchana, R., and M. Punithavalli. "Web Usage Mining for Predicting Users’ Browsing Behaviors by using FPCM Clustering." International Journal of Engineering and Technology 3, no. 5 (2011): 491–96. http://dx.doi.org/10.7763/ijet.2011.v3.275.
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Wang, Shuli, Xuewen Li, Xiaomeng Kou, Jin Zhang, Shaojie Zheng, Jinlong Wang, and Jibing Gong. "Sequential Recommendation through Graph Neural Networks and Transformer Encoder with Degree Encoding." Algorithms 14, no. 9 (August 31, 2021): 263. http://dx.doi.org/10.3390/a14090263.
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Predicting users’ next behavior through learning users’ preferences according to the users’ historical behaviors is known as sequential recommendation. In this task, learning sequence representation by modeling the pairwise relationship between items in the sequence to capture their long-range dependencies is crucial. In this paper, we propose a novel deep neural network named graph convolutional network transformer recommender (GCNTRec). GCNTRec is capable of learning effective item representation in a user’s historical behaviors sequence, which involves extracting the correlation between the target node and multi-layer neighbor nodes on the graphs constructed under the heterogeneous information networks in an end-to-end fashion through a graph convolutional network (GCN) with degree encoding, while the capturing long-range dependencies of items in a sequence through the transformer encoder model. Using this multi-dimensional vector representation, items related to a user historical behavior sequence can be easily predicted. We empirically evaluated GCNTRec on multiple public datasets. The experimental results show that our approach can effectively predict subsequent relevant items and outperforms previous techniques.
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Salameh, Rana, and Christian Sebastian Loh. "Engagement and Players' Intended Behaviors in a Cybersecurity Serious Game." International Journal of Gaming and Computer-Mediated Simulations 14, no. 1 (January 1, 2022): 1–21. http://dx.doi.org/10.4018/ijgcms.313185.
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Serious games have been shown to be effective in engaging end-users for various types of training. However, the research in cybersecurity awareness training with serious games is scarce. The authors are interested in (1) the engagement factors that could predict users' intended behavior after learning and (2) whether or not playing a game repeatedly can affect engagement. They assessed players' coping and threat appraisal and measured their multidimensional (i.e., cognitive, affective, behavioral) engagement in cybersecurity awareness. The participants (N=122) in this experiment were randomly assigned to either three or five rounds of gameplay of a commercial cybersecurity awareness serious game. The findings revealed that users' engagement levels were significantly better sustained through five sessions of gameplay with cognitive (but not affective or behavioral) engagement. Serious game developers should include more activities in the cognitive dimension, rather than the affective or behavioral dimensions to assure high engagement and influence the intended cybersecurity awareness behaviors.
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Zhang, Lu Fang, and Chen Xi Fan. "Home Leisure Seat Design Based on Leisure Behavior." Applied Mechanics and Materials 268-270 (December 2012): 1954–57. http://dx.doi.org/10.4028/www.scientific.net/amm.268-270.1954.
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This paper presents the influence of leisure behavior and sitting posture to the design of home seat. With the help of questionnaire and interview, we explored the users' preferences of home leisure behaviors, their function requirements and usage of home leisure seats. Through research of the users’ sitting postures of different leisure behavior and comfort of different sitting posture, our study shows function requirements and sitting posture differences exist according to different favor of home leisure behaviors. Some suggestions are given for home leisure seat design, considering the differences of home leisure behaviors.
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Mortazavi, Marjan, Mohammad Rahim Esfidani, and Ali Shaemi Barzoki. "Influencing VSN users’ purchase intentions." Journal of Research in Interactive Marketing 8, no. 2 (June 3, 2014): 102–23. http://dx.doi.org/10.1108/jrim-08-2013-0057.
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Purpose – The purpose of this paper is to examine the characteristics of virtual social networks (VSNs) and to determine their salient attributes, including those that influence flow experience, trust and electronic word-of-mouth (eWOM) behaviors affecting users’ purchase intentions, and to provide important strategic implications contributing to the Internet marketing literature. Design/methodology/approach – Using a self-administered questionnaire, the data (n = 167) are collected from two Iranian Internet social networking sites, namely, facenama.com and cloob.com . Using LISREL 8.5, hypothesized relationships are examined through structural equation modeling (SEM) analysis. Findings – According to the results of the study, despite all assumptions and studies to the contrary, eWOM behaviors in VSNs are derived from neither users’ flow experience nor their trust in VSNs, but they are mostly caused by VSNs’ attributes, from which four are investigated in this study, namely, communication and social relationships, entertainment, information disclosure and ease of use. Nevertheless, according to the results, VSN attributes also influence trust and flow experience, trust in a VSN environment influences users’ flow experience and eWOM in VSNs has significant impact on users’ purchase intentions. The findings also revealed that the level of education of a user affects how much he trusts the VSN environment. Practical implications – The author examined flow experience, trust, eWOM behavior, purchase intentions and the VSNs’ attributes to verify their relationships, providing a better understanding of an effective indirect marketing in VSNs. The results also have important implications for researchers. Originality/value – While flow experience, trust, word-of-mouth (WOM) behaviors and purchase intentions have been separately studied in Web sites, e-shopping malls and blogs, little research has sought to identify the existence of these elements within VSNs, their correlations with one another and how they are affected by VSNs’ attributes.
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Huang, Xiaowen, Jitao Sang, Jian Yu, and Changsheng Xu. "Learning to Learn a Cold-start Sequential Recommender." ACM Transactions on Information Systems 40, no. 2 (April 30, 2022): 1–25. http://dx.doi.org/10.1145/3466753.
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The cold-start recommendation is an urgent problem in contemporary online applications. It aims to provide users whose behaviors are literally sparse with as accurate recommendations as possible. Many data-driven algorithms, such as the widely used matrix factorization, underperform because of data sparseness. This work adopts the idea of meta-learning to solve the user’s cold-start recommendation problem. We propose a meta-learning-based cold-start sequential recommendation framework called metaCSR, including three main components: Diffusion Representer for learning better user/item embedding through information diffusion on the interaction graph; Sequential Recommender for capturing temporal dependencies of behavior sequences; and Meta Learner for extracting and propagating transferable knowledge of prior users and learning a good initialization for new users. metaCSR holds the ability to learn the common patterns from regular users’ behaviors and optimize the initialization so that the model can quickly adapt to new users after one or a few gradient updates to achieve optimal performance. The extensive quantitative experiments on three widely used datasets show the remarkable performance of metaCSR in dealing with the user cold-start problem. Meanwhile, a series of qualitative analysis demonstrates that the proposed metaCSR has good generalization.
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Han, Ye, and T. Selwyn Ellis. "A Study of User Continuance Behavioral Intentions Toward Privacy-Protection Practices." Information Resources Management Journal 31, no. 2 (April 2018): 24–46. http://dx.doi.org/10.4018/irmj.2018040102.
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Prior research on privacy protective behaviors has found that online users irrationally trade protection for convenience, and so act against their own privacy preferences. The present article uses expectancy-confirmation theory (ECT) models to explain the continuance behavioral intentions of online users toward privacy-protection practices. It redefines convenience to highlight human behaviors involved in various stages of implementing privacy practices processes. The results show that earlier privacy practice experiences impact the present as well as the future protective behaviors of users, and that convenience-orientation is an important aspect of human nature that should not be inhibited by complex privacy practices. Therefore, to serve online users better, both researchers and practitioners should consider the personal perceptions of convenience of online users when constructing their privacy practices.