Inactive Users Research Articles

Gamify4LexAmb: a gamification-based approach to address lexical ambiguity in natural language requirements.

Ambiguity is a common challenge in specifying natural language (NL) requirements. One of the reasons for the occurrence of ambiguity in software requirements is the lack of user involvement in requirements elicitation and inspection phases. Even if they get involved, it is hard for them to understand the context of the system, and ultimately unable to provide requirements correctly due to a lack of interest. Previously, the researchers have worked on ambiguity avoidance, detection, and removal techniques in requirements. Still, less work is reported in the literature to actively engage users in the system to reduce ambiguity at the early stages of requirements engineering. Traditionally, ambiguity is addressed during inspection when requirements are initially specified in the SRS document. Resolving or removing ambiguity during the inspection is time-consuming, costly, and laborious. Also, traditional elicitation techniques have limitations like lack of user involvement, inactive user participation, biases, incomplete requirements, etc. Therefore, in this study, we have designed a framework, Gamif ication for Lex ical Amb iguity (Gamify4LexAmb), for detecting and reducing ambiguity using gamification. Gamify4LexAmb engages users and identifies lexical ambiguity in requirements, which occurs in polysemy words where a single word can have several different meanings. We have also validated Gamify4LexAmb by developing an initial prototype. The results show that Gamify4LexAmb successfully identifies lexical ambiguities in given requirements by engaging users in requirements elicitation. In the next part of our research, an industrial case study will be performed to understand the effects of gamification on real-time data for detecting and reducing NL ambiguity.

Using social Cognitive theory to reengage dormant users in question and answer Communities: A case study of active StackOverflow participants

Online question-and-answer communities are seriously threatened by low user participation. There is currently a rare comprehensive study on the knowledge contribution pattern of consistently active participants and the moderating role of peer recognition, which can help improve low participation and reengage inactive users, despite researchers having examined the various facets of knowledge contribution and made helpful suggestions. As per the self-determination and social cognitive theory, the communal environment impacts peers and imitates role models or reliable sources in their involvement patterns. We have examined StackOverflow’s most reliable active users from 2010 to 2020 using the social cognition and self-determination theories to use the findings to reactivate dormant users. We have used a two-step dynamic system GMM model to get robust and reliable findings. The research discovered that peer repudiation, reputation, and online social interactions favorably affect the contributed knowledge. However, knowledge-seeking and earning virtual badges such as gold and bronze usually negatively impact it. Furthermore, it was revealed that the effect of virtual badges on contributed knowledge was positively moderated by peer recognition. However, peer recognition reduces the benefits of social interaction and reputation on the contributed knowledge. The study’s findings advance the body of knowledge and provide thorough management implications for raising low participation, reengaging inactive users, and cultivating a culture of innovative sharing of knowledge.

A Fairness Group Recommendation Algorithm Based On User Activity

In the process of group recommendation, due to the different preferences of group members, the recommendation results cannot meet the needs of all users. How to maximize the fairness of group recommendation is still a challenge. Therefore, this paper proposes a group recommendation algorithm based on user activity. Firstly, a group discovery algorithm based on item cluster preference was used to mine potential groups. Secondly, considering the dynamic change of activity, a sliding time window is designed to investigate the recent activity of each member in the group at the time of subgroup division, and the group is divided into active subgroup and inactive subgroup. Finally, the group recommendation list was generated by aggregating the subgroup preferences by average consensus. Experimental results on the public dataset show that compared with the AGREE algorithm, the recommendation accuracy and coverage of the proposed algorithm are improved by 2.1% and 2.9%, respectively. By focusing on the preference needs of inactive users, the proposed algorithm effectively improves the recommendation satisfaction and group fairness.

Improving Graph Collaborative Filtering with Directional Behavior Enhanced Contrastive Learning

Graph Collaborative Filtering is a widely adopted approach for recommendation, which captures similar behavior features through Graph Neural Network (GNN). Recently, Contrastive Learning (CL) has been demonstrated as an effective method to enhance the performance of graph collaborative filtering. Typically, CL-based methods first perturb users’ history behavior data (e.g., drop clicked items), then construct a self-discriminating task for behavior representations under different random perturbations. However, for widely existing inactive users, random perturbation makes their sparse behavior information more incomplete, thereby harming the behavior feature extraction. To tackle the above issue, we design a novel directional perturbation-based CL method to improve the graph collaborative filtering performance. The idea is to perturb node representations through directionally enhancing behavior features. To do so, we propose a simple yet effective feedback mechanism, which fuses the representations of nodes based on behavior similarity. Then, to avoid irrelevant behavior preferences introduced by the feedback mechanism, we construct a behavior self-contrast task before and after feedback, to align the node representations between the final output and the first layer of GNN. Different from the widely adopted self-discriminating task, the behavior self-contrast task avoids complex message propagation on different perturbed graphs, which is more efficient than previous methods. Extensive experiments on three public datasets demonstrate that the proposed method has distinct advantages over other CL methods on recommendation accuracy.

Mitigating Recommendation Biases via Group-Alignment and Global-Uniformity in Representation Learning

Collaborative Filtering (CF) plays a crucial role in modern recommender systems, leveraging historical user-item interactions to provide personalized suggestions. However, CF-based methods often encounter biases due to imbalances in training data. This phenomenon makes CF-based methods tend to prioritize recommending popular items and performing unsatisfactorily on inactive users. Existing works address this issue by rebalancing training samples, reranking recommendation results, or making the modeling process robust to the bias. Despite their effectiveness, these approaches can compromise accuracy or be sensitive to weighting strategies, making them challenging to train. Therefore, exploring how to mitigate these biases remains in urgent demand. In this paper, we deeply analyze the causes and effects of the biases and propose a framework to alleviate biases in recommendation from the perspective of representation distribution, namely Group- A lignment and Global- U niformity Enhanced R epresentation L earning for Debiasing Recommendation (AURL) . Specifically, we identify two significant problems in the representation distribution of users and items, namely group-discrepancy and global-collapse. These two problems directly lead to biases in the recommendation results. To this end, we propose two simple but effective regularizers in the representation space, respectively named group-alignment and global-uniformity. The goal of group-alignment is to bring the representation distribution of long-tail entities closer to that of popular entities, while global-uniformity aims to preserve the information of entities as much as possible by evenly distributing representations. Our method directly optimizes both the group-alignment and global-uniformity regularization terms to mitigate recommendation biases. Please note that AURL applies to arbitrary CF-based recommendation backbones. Extensive experiments on three real datasets and various recommendation backbones verify the superiority of our proposed framework. The results show that AURL not only outperforms existing debiasing models in mitigating biases but also improves recommendation performance to some extent.

RFM Segmentation Analysis for Determine Online Marketing Strategy: the Soul Coffee Mate Case Study

This study explores the impact of digital marketing on a cafe named The Soul, located in Jakarta. The cafe experienced a recent decline in sales attributed to factors such as new competitors, closures of nearby establishments, diminishing trends in working from cafes, and changes in customer demographics. Recognizing the need for a robust online marketing strategy, researchers aim to analyze customer segmentation using the RFM model, focusing on Recency, Frequency, and Monetary aspects of transactions over the past 11 months. The goal is to understand customer behavior, design effective online marketing strategies, and ultimately boost the company's revenue. The RFM analysis results identified eight customer segments for The Soul: Soulmate, Loyal Customers, Potential Loyalists, Promising, New User, At Risk, Can't Lose Them, and Hibernating. Proposed strategies include personalized promotions for different customer segments, such as monthly appreciation for "Soulmate" customers, discounts for loyal customers, and targeted communication for new or inactive users. These strategies aim to enhance customer engagement, loyalty, and satisfaction through digital promotions and personalized interactions. Keywords: Cafe Business, Customer Behavior, Customer Segmentation, Customer Loyalty, Digital Marketing, Online Promotion, RFM Model, Sales Analysis

The effectiveness of mobile app usage in facilitating weight loss: An observational study.

With increasing rates of global obesity and associated health issues, there is an ever-increasing need for weight management solutions to be more accessible. Mobile applications offer accessible support systems and have the potential to offer a viable and effective weight management solution as an alternative to traditional healthcare models. To evaluate the effectiveness of the SIMPLE mobile application for time-restricted eating in achieving weight loss (WL). User data were analyzed between January 2021 and January 2023. In-app activity was calculated as the proportion of active days over 12, 26 and 52weeks. A day is considered active if it contains at least one in-app action (e.g., logging weight, food, fasting, or physical activity). Users were categorized into four in-app activity levels: inactive (in-app activity <33%), medium activity (33%-66%), high activity (66%-99%), and maximal activity (100%). Weight change among in-app activity groups was assessed at 12, 26, and 52weeks. Out of 53,482 users, a positive association was found between the use of the SIMPLE app and WL. Active app users lost more weight than their less active counterparts. Active users had a median WL of 4.20%, 5.04%, and 3.86% at 12, 26, and 52weeks, respectively. A larger percentage of active users-up to 50.26%-achieved clinically significant WL (≥5%) when compared to inactive users. A dose-response relationship between WL and app usage was found after adjusting for gender, age, and initial Body Mass Index; a 10% increase in app activity correlated with increased WL by 0.43, 0.66 and 0.69kg at 12, 26, and 52weeks, respectively. The study demonstrates that the SIMPLE app enables effective WL directly associated with the level of app engagement. Mobile health applications offer an accessible and effective weight management solution and should be considered when supporting adults to lose weight.

Implementasi Authentication Captive Portal Pada Wireless Local Area Network di PT. St. Morita Industries

PT. St. Morita Industries has a Wireless Local Area Network (WLAN) which is used as a medium for exchanging data and information by utilizing wireless transmission media, WLAN currently uses WPA2-PSK as a security system to authenticate users to access the internet. However, the use of WPA2-PSK as WLAN security still has a weakness due to the use of the same 1 password for many users in order to connect to the WLAN hotspot will be an opportunity for cyber crime. This happens because it will be very easy for irresponsible users to enter the WLAN. Therefore, in this study, captive portal authentication will be applied as an effort to increase WLAN security replaces WPA2-PSK. This research process uses the Network Development Life Cycle (NDLC) method. All the configurations needed to build the captive portal authentication take advantage of the Winbox program. This research has resulted in a special user authentication limitation for users who have registered on the WLAN is permitted to access this company's internet. In addition, the Winbox program can also be used for monitoring all users connected to the WLAN, both active and inactive users. By setting up captive portal authentication on a WLAN network using a proxy routerboard device which comprises an IP address, DHCP server, hostpot setup, NAT firewall, and DNS server one can create a WPA2-PSK security system. This configuration is completed with the aid of the Winbox v3.37 software.

Willingness to Keep or to Give Up the Social Media for a Certain Monetary Compensation

Millions of people around the world use social media every day for various reasons, whether it is sharing information, self-presentation, filling free time, or communicating with friends. The use of social networks brings with it advantages, but also risks associated with information overload, loss of privacy, as well as fraud or criminal acts. Mostly young people are connected to social networks almost constantly and find it more difficult to give up using social networks compared to older age groups. The aim of the present paper is to find out the willingness of social network users to give up social network such as Facebook and Instagram for certain monetary compensation. The necessary data were collected based on a questionnaire survey within 4 months. A total of 2 095 responses were collected. The static methods Chi-Square test of independence, logistic regression and the statistical program SAS Enterprise Guide were used to evaluate the results of the questionnaire survey. Analysis of Maximum Likelihood Estimates was tested for five predictors: Age, Gender, Income, Activity on the social network, Number of subscribers on the social network, especially for the social network Facebook and Instagram. Based on the results of the questionnaire survey, it can be concluded that Facebook users (in all age groups and regardless of gender) demand a higher average monthly financial compensation for giving up the social network compared to Instagram users. The same conclusion follows when categorizing respondents into age categories. Regarding gender, women demand lower monetary compensation than men in relation to both social networks. There was a significant difference in the Activity predictor, where active users asked for a higher amount of money for the Instagram social network and, conversely, inactive users asked for a higher average monthly payment for the Facebook social network.&#x0D; &#x0D; Received: 25 July 2023 / Accepted: 20 November 2023 / Published: 5 January 2024

MFM: A Multiple-Features Model for Leisure Event Recommendation in Geotagged Social Networks

Event-based social networks (EBSNs) are rich in information about users and leisure events. The willingness of users to participate in leisure events is influenced by many factors such as event time, location, content, organizer, and social relationship factors of users. Event recommendation systems in EBSNs can help leisure event organizers to accurately find users who want to participate in events. However, to address the existing cold-start problems and improve the accuracy of event recommendations, we propose a multiple-feature-based leisure event recommendation model (MFM). We introduce the user’s social contacts into the user preference features and construct a user feature space by integrating the features of the user preferences for events and organizers and preferences of the user’s closest friends. Moreover, considering the behavioral differences between active and inactive users, we extracted the respective features and trained the feature weight models. Finally, the experimental results showed that in comparison with the baseline models, the precision of the MFM is higher by at least 7.9%.

Measuring similarity based on user activeness in recommender systems to improve algorithm scalability

Recommender systems analyze user preferences and predict their preferred products based on available data, saving users time in the search for preferred products. However, accurate recommendations for both the system and the user may require complicated computations or the need to store more neighbors, leading to a less scalable algorithm. To address this issue, we define user activity coefficients and then analyze the relationship between different activity coefficients and similarity to design a novel approach for measuring user activity-based similarity in recommender systems. By examining the similarity of inactive user groups, our approach considers the accurate calculation of their similarity as one of the keys to influencing the number of required neighbors for optimal performance. As compared to several advanced collaborative filtering algorithms on two datasets, the algorithm developed in this paper can reduce the number of neighbors by 35.58%–75.38% while maintaining high prediction accuracy. The results indicate that user activity in recommender systems has a significant impact on similarity evaluation, which should be considered in relevant models.

Variational Bayesian Inference Clustering-Based Joint User Activity and Data Detection for Grant-Free Random Access in mMTC

Tailor-made for massive connectivity and sporadic access, grant-free random access has become a promising candidate access protocol for massive machine-type communications (mMTC). Compared with conventional grant-based protocols, grant-free random access skips the exchange of scheduling information to reduce the signaling overhead, and facilitates sharing of access resources to enhance access efficiency. However, some challenges remain to be addressed in the receiver design, such as unknown identity of active users and multi-user interference (MUI) on shared access resources. In this work, we deal with the problem of joint user activity and data detection for grant-free random access. Specifically, the approximate message passing (AMP) algorithm is first employed to mitigate MUI and decouple the signals of different users. Then, we extend the data symbol alphabet to incorporate the null symbols from inactive users. In this way, the joint user activity and data detection problem is formulated as a clustering problem under the Gaussian mixture model. Furthermore, in conjunction with the AMP algorithm, a variational Bayesian inference based clustering (VBIC) algorithm is developed to solve this clustering problem. Simulation results show that, compared with state-of-art solutions, the proposed AMP-combined VBIC (AMP-VBIC) algorithm achieves a significant performance gain in detection accuracy.

Impact of a digital web-based asthma platform, a real-life study

BackgroundDigital health technology (DHT) is a growing area in the treatment of chronic diseases. Study results on DHT’s effect on asthma control have been mixed, but benefits have been seen for adherence, self-management, symptoms, and quality of life. The aim was to evaluate the impact of an interactive web-based asthma treatment platform on asthma exacerbations and health care visits.MethodsIn this real-life study, we retrospectively collected data on adult patients registered on a web-based interactive asthma treatment platform between December 2018 and May 2021. Patients who activated their accounts were active users, and patients who did not were inactive users and considered as controls. We compared the number of exacerbations, total number of exacerbation events defined as the sum of oral corticosteroid (OCS) and antimicrobial courses, emergency room visits, hospitalizations, and asthma-related health care visits before and one year after the registration on the platform. Statistical tests used included the t-test, Pearson’s chi-square test and Poisson regression models.ResultsOf 147 patients registered on the platform, 106 activated their accounts and 41 did not. The active users had significantly fewer total number of exacerbation events (2.56 per person years, relative decline 0.78, 95% CI 0.6 to 1.0) and asthma-related health care visits (2.38 per person years, relative decline 0.84, 95% CI 0.74 to 0.96) than before registration to the platform, whereas the reductions in health care visits and the total number of exacerbation events were not significant in the inactive users.ConclusionsAn interactive web-based asthma platform can reduce asthma-related health care visits and exacerbations when used actively.

Understanding of the dynamics of mobile reading: An HMM model of user engagement and content consumption

AbstractUnderstanding consumers’ engagement and subsequent content consumption behavior in the mobile context is critical to mobile app providers. In this paper, we develop a Hidden Markov Model (HMM) to capture the dynamics of users’ engagement states and consumption decisions on the number of books/chapters read and the amount of money spent. Our method allows us to simultaneously capture three interdependent usage behaviors using a single integrated model and identify the impact of content loading time and previous reading behavior on users’ engagement dynamics and content consumption. We calibrate the model using a tap stream data set of individual users’ reading activities on a mobile app. Our analysis reveals three distinct engagement states, a low state with inactive users, a medium state with users sampling books, and a high state with users reading intensively. Furthermore, we find that content loading time has higher negative impacts on high‐state users in state transitioning than medium‐state users. In contrast, the days that elapsed since the last visit has a similar negative impact on the users in the high and medium states. The effect of usage frequency on users in state transitioning is always positive. We have also identified the weekend effect and social influence on users’ reading outcomes. Finally, our simulations quantify the shortened content loading time and the days elapsed since the last visit on users’ engagement dynamics and content consumption decisions, which generate important managerial implications for app providers.

Toward Federated Learning With Byzantine and Inactive Users: A Game Theory Approach

Federated learning (FL) can guarantee privacy by allowing local users only upload their training models to central server (CS). However, the existence of Byzantine or inactive users may cause model corruption or inactively participation in FL. In this paper, a game theory based detection and incentive method is designed for Byzantine and inactive users. Specifically, a differential aggregate gradient descent (DAGD) algorithm is adopted to improve the stability and fasten the convergence. Then the loss function is modified by considering Byzantine and inactive users. For Byzantine users, a random Euclidean distance (RED) voting method is designed to identify Byzantine users, and after identification, Byzantine users are motivated by the game theory. For inactive users, when inactive users are detected by contribution, Nash equilibrium with a mixed strategy is used to solve the problem of inactive users’ participation in FL. Extensive experimental results show that Byzantine users and inactive users can be detected and motivated by the algorithm. Meanwhile, compared with other methods, the accuracy of the optimized model is improved with reduced training time.

Online Parasites: Concept, Characteristics, and Implications for Business Communication

Objectives: This research, using the interview methods, attempts to identify a new type of network users which is called as “online parasites.” Since online parasites have a higher level of information retention and activity than ordinary passive users, companies that operate online communities should consider the needs and meaning of “online parasites” when activating inactive users. Methods: This study conducted preliminary interviews with seven participants to observe how they use the internet, why they join the online community, and how they obtain information from the online community, regardless of whether they have the experience in posting messages in the online community. Results: This study explores the relationship between the active user, inactive user, lurker, and online parasite according to users’ interaction behaviors and the perceived contributions to the community. In addition, online parasites are defined as users who access and use the online community and produce interactive behaviors (such as likes and favorites) with the content but have no substantial content creation and contribution. Conclusions: Compared to Lurker or passive receivers who read newspapers many years ago, most internet users now have a certain degree of enthusiasm like online parasites. Therefore, conceptualizing online parasites and studying their motivations and behaviors will greatly impact the sustainable development of the internet and virtual community.

The Effects of Digitally Delivered Nudges in a Corporate Wellness Program

We investigate how two digitally delivered nudges, namely light social support (nonverbal cues such as kudos or likes) and motivational messaging, affect employees’ self-reported physical activity in an online, corporate wellness program. Within this unique field setting, using data from several years, we found evidence that both types of nudges provide benefits beyond the effect of cash incentives. However, the effects vary by individual, depending on whether the employee is actively engaging in physical activity, and by time, depending on how long the employee has been in the wellness program. We found light social support to be less effective over time, while motivational messages were found to be more effective with the duration in the program and generally more effective for physically inactive users. Our findings have implications for the design of wellness systems, suggesting different approaches depending on an employee’s current activity level and tenure in the program

Data-Aided Active User Detection With False Alarm Correction in Grant-Free Transmission

In most existing grant-free (GF) studies, the two key tasks, namely active user detection (AUD) and payload data decoding, are handled separately. In this letter, a two-step data-aided AUD scheme is proposed, namely the initial AUD step and the false alarm correction step respectively. To implement the initial AUD step, an embedded low-density-signature (LDS) based preamble pool is constructed. In addition, two message passing algorithm (MPA) based initial estimators are developed. In the false alarm correction step, a redundant factor graph is constructed based on the initial active user set, on which MPA is employed for data decoding. The remaining false detected inactive users will be further detected by the false alarm corrector with the aid of decoded data symbols. Simulation results reveal that both the data decoding performance and the AUD performance are significantly enhanced by more than 1.5 dB at the target accuracy of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10^{-3}$ </tex-math></inline-formula> compared with the traditional compressed sensing (CS) based counterparts.

Fasting and weight loss: mobile application-based approach.

The purpose of this study was to investigate the efficacy of intermittent fasting (IF) guidance, delivered through a smartphone application, in terms of engagement and weight loss. We performed a retrospective chart-review study of all consecutive users with overweight or obesity of the DoFasting mobile application, which integrates IF solutions for people looking to lose weight. Users with overweight and obesity at the beginning of application use, who met the inclusion criteria (entered their gender, height, and starting weight; had more than one weight entry; used the app for at least a month; had at least one active day per week with valid app-related activities) and used the application for weight loss were included in the study. The final study cohort comprised 22,022 consecutive users. The short-term cohort comprised 17,221 users (8585 females and 8636 males), while 4801 users (2983 females and 1818 males) were in the long-term cohort. Long-term active users of the application lost a statistically significant amount of weight over the study period and lost more than inactive users did (2.2 [3.5] vs 1.4 [3.2], p < 0.0001; the results are shown in kilograms as medians with the interquartile range in brackets). Active and long-term users were also more likely to reduce their BMI class (p < 0.05) in comparison to non-active short-term users. A multiple regression model showed that the total length of use and active days were the most significant predictors of weight loss. In-app activities such as total fasting hours, weight logging, completing challenges, and providing feedback were also statistically significant predictors of weight loss with varying contributions. We found that the DoFasting mobile application that implements IF is an effective tool for weight loss if used actively and sufficiently. This retrospective chart review study was approved by BRANY IRB in January 2022 (study ID.: 22-08-034-939).

The law of non-usage attrition in a technology-based behavioral intervention for black adults with poor cardiovascular health.

Digital health innovations, such as telehealth and remote monitoring, have shown promise in addressing patient barriers to accessing evidence-based programs and providing a scalable path for tailored behavioral interventions that support self-management skills, knowledge acquisition and promotion of relevant behavioral change. However, significant attrition continues to plague internet-based studies, a result we believe can be attributed to characteristics of the intervention, or individual user characteristics. In this paper, we provide the first analysis of determinants of non usage attrition in a randomized control trial of a technology-based intervention for improving self-management behaviors among Black adults who face increased cardiovascular risk factors. We introduce a different way to measure nonusage attrition that considers usage over a specific period of time and estimate a cox proportional hazards model of the impact of intervention factors and participant demographics on the risk of a nonusage event. Our results indicated that not having a coach (compared to having a coach) decreases the risk of becoming an inactive user by 36% (HR = .63, P = 0.04). We also found that several demographic factors can influence Non-usage attrition: The risk of nonusage attrition amongst those who completed some college or technical school (HR = 2.91, P = 0.04) or graduated college (HR = 2.98, P = 0.047) is significantly higher when compared to participants who did not graduate high school. Finally, we found that the risk of nonsage attrition among participants with poor cardiovascular from "at-risk" neighborhoods with higher morbidity and mortality rates related to CVD is significantly higher when compared to participants from "resilient" neighborhoods (HR = 1.99, P = 0.03). Our results underscore the importance of understanding challenges to the use of mhealth technologies for cardiovascular health in underserved communities. Addressing these unique barriers is essential, because a lack of diffusion of digital health innovations exacerbates health disparities.