scholarly journals Incremental Fairness in Two-Sided Market Platforms: On Smoothly Updating Recommendations

2020 ◽  
Vol 34 (01) ◽  
pp. 181-188 ◽  
Author(s):  
Gourab K. Patro ◽  
Abhijnan Chakraborty ◽  
Niloy Ganguly ◽  
Krishna Gummadi
Keyword(s):  
Customer Satisfaction ◽  
Real World ◽  
Well Being ◽  
Online Optimization ◽  
Smooth Transition ◽  
Goods And Services ◽  
Online Platforms ◽  
Customer Evaluations ◽  
Abrupt Changes ◽  
Real World Datasets

Major online platforms today can be thought of as two-sided markets with producers and customers of goods and services. There have been concerns that over-emphasis on customer satisfaction by the platforms may affect the well-being of the producers. To counter such issues, few recent works have attempted to incorporate fairness for the producers. However, these studies have overlooked an important issue in such platforms -- to supposedly improve customer utility, the underlying algorithms are frequently updated, causing abrupt changes in the exposure of producers. In this work, we focus on the fairness issues arising out of such frequent updates, and argue for incremental updates of the platform algorithms so that the producers have enough time to adjust (both logistically and mentally) to the change. However, naive incremental updates may become unfair to the customers. Thus focusing on recommendations deployed on two-sided platforms, we formulate an ILP based online optimization to deploy changes incrementally in η steps, where we can ensure smooth transition of the exposure of items while guaranteeing a minimum utility for every customer. Evaluations over multiple real world datasets show that our proposed mechanism for platform updates can be efficient and fair to both the producers and the customers in two-sided platforms.

scholarly journals Toward Fair Recommendation in Two-sided Platforms

2022 ◽  
Vol 16 (2) ◽  
pp. 1-34
Author(s):  
Arpita Biswas ◽  
Gourab K. Patro ◽  
Niloy Ganguly ◽  
Krishna P. Gummadi ◽  
Abhijnan Chakraborty
Keyword(s):  
Customer Satisfaction ◽  
Real World ◽  
Computation Time ◽  
Well Being ◽  
Personalized Recommendation ◽  
Indivisible Goods ◽  
Goods And Services ◽  
Online Platforms ◽  
Real World Datasets ◽  
The Cost

Many online platforms today (such as Amazon, Netflix, Spotify, LinkedIn, and AirBnB) can be thought of as two-sided markets with producers and customers of goods and services. Traditionally, recommendation services in these platforms have focused on maximizing customer satisfaction by tailoring the results according to the personalized preferences of individual customers. However, our investigation reinforces the fact that such customer-centric design of these services may lead to unfair distribution of exposure to the producers, which may adversely impact their well-being. However, a pure producer-centric design might become unfair to the customers. As more and more people are depending on such platforms to earn a living, it is important to ensure fairness to both producers and customers. In this work, by mapping a fair personalized recommendation problem to a constrained version of the problem of fairly allocating indivisible goods, we propose to provide fairness guarantees for both sides. Formally, our proposed FairRec algorithm guarantees Maxi-Min Share of exposure for the producers, and Envy-Free up to One Item fairness for the customers. Extensive evaluations over multiple real-world datasets show the effectiveness of FairRec in ensuring two-sided fairness while incurring a marginal loss in overall recommendation quality. Finally, we present a modification of FairRec (named as FairRecPlus ) that at the cost of additional computation time, improves the recommendation performance for the customers, while maintaining the same fairness guarantees.

The impact of algorithmic decision-making processes on young people’s well-being (Preprint)

2019 ◽  
Author(s):  
Elvira Perez Vallejos ◽  
Liz Dowthwaite ◽  
Helen Creswich ◽  
Virginia Portillo ◽  
Ansgar Koene ◽  
...  
Keyword(s):  
Young People ◽  
Significant Proportion ◽  
Well Being ◽  
Early Years ◽  
Negative Experiences ◽  
Children And Young People ◽  
Online Platforms ◽  
Internet Users ◽  
Decision Making Processes ◽  
The Impact

BACKGROUND Algorithms rule the online environments and are essential for performing data processing, filtering, personalisation and other tasks. Research has shown that children and young people make up a significant proportion of Internet users, however little attention has been given to their experiences of algorithmically-mediated online platforms, or the impact of them on their mental health and well-being. The algorithms that govern online platforms are often obfuscated by a lack of transparency in their online Terms and Conditions and user agreements. This lack of transparency speaks to the need for protecting the most vulnerable users from potential online harms. OBJECTIVE To capture young people's experiences when being online and perceived impact on their well-being. METHODS In this paper, we draw on qualitative and quantitative data from a total of 260 children and young people who took part in a ‘Youth Jury’ to bring their opinions to the forefront, elicit discussion of their experiences of using online platforms, and perceived psychosocial impact on users. RESULTS The results of the study revealed the young people’s positive as well as negative experiences of using online platforms. Benefits such as being convenient and providing entertainment and personalised search results were identified. However, the data also reveals participants’ concerns for their privacy, safety and trust when online, which can have a significant impact on their well-being. CONCLUSIONS We conclude by making recommendations that online platforms acknowledge and enact on their responsibility to protect the privacy of their young users, recognising the significant developmental milestones that this group experience during these early years, and the impact that technology may have on them. We argue that governments need to incorporate policies that require technologists and others to embed the safeguarding of users’ well-being within the core of the design of Internet products and services to improve the user experiences and psychological well-being of all, but especially those of children and young people. CLINICALTRIAL N/A

Social Protection, Adolescent Well-Being, and Development in Low- and Middle-Income Countries

2018 ◽  
Author(s):  
Suman Verma
Keyword(s):  
Social Inclusion ◽  
Social Protection ◽  
Well Being ◽  
Middle Income ◽  
Developmental Potential ◽  
Goods And Services ◽  
Middle Income Countries ◽  
Social Protection Systems ◽  
Cycle Of Poverty ◽  
Low And Middle Income

Effective social protection policies are crucial to realizing adolescents’ rights, ensuring their well-being, breaking the cycle of poverty and vulnerability, and helping them realize their full developmental potential. Low- and middle-income countries (LMICs) have extended social security coverage to ensure basic protections—while continuing to develop social protection systems. Social protection for LMIC adolescents in the context of gross violations of their basic rights is examined. Prevalence, consequences of protection rights violations, and the role and impact of social protection programs in ensuring enhanced opportunities for development and well-being among young people are discussed. Results demonstrate direct impacts (e.g., increased income, consumption, goods and services access; greater social inclusion; reduced household stress). LMICs need integrated social protection policy and program expansion if the 2030 Agenda for Sustainable Development is to be realized. With adolescent-centered policies and investments, governments can help adolescents realize their rights to a fulfilling and productive life.

Time-Efficient Ensemble Learning with Sample Exchange for Edge Computing

2021 ◽  
Vol 21 (3) ◽  
pp. 1-17
Author(s):  
Wu Chen ◽  
Yong Yu ◽  
Keke Gai ◽  
Jiamou Liu ◽  
Kim-Kwang Raymond Choo
Keyword(s):  
Ensemble Learning ◽  
Real World ◽  
Interaction Mechanism ◽  
Training Model ◽  
Edge Computing ◽  
Learning Techniques ◽  
Multi Agent ◽  
Real World Datasets ◽  
Entire Dataset ◽  
Exchange Data

In existing ensemble learning algorithms (e.g., random forest), each base learner’s model needs the entire dataset for sampling and training. However, this may not be practical in many real-world applications, and it incurs additional computational costs. To achieve better efficiency, we propose a decentralized framework: Multi-Agent Ensemble. The framework leverages edge computing to facilitate ensemble learning techniques by focusing on the balancing of access restrictions (small sub-dataset) and accuracy enhancement. Specifically, network edge nodes (learners) are utilized to model classifications and predictions in our framework. Data is then distributed to multiple base learners who exchange data via an interaction mechanism to achieve improved prediction. The proposed approach relies on a training model rather than conventional centralized learning. Findings from the experimental evaluations using 20 real-world datasets suggest that Multi-Agent Ensemble outperforms other ensemble approaches in terms of accuracy even though the base learners require fewer samples (i.e., significant reduction in computation costs).

scholarly journals OFCOD: On the Fly Clustering Based Outlier Detection Framework

Data ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Ahmed Elmogy ◽  
Hamada Rizk ◽  
Amany M. Sarhan
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Intrusion Detection ◽  
Real Time ◽  
Outlier Detection ◽  
Real World ◽  
Medical Data ◽  
Experimental Results ◽  
Real Time Applications ◽  
Real World Datasets

In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics.

‘Who unlocked the kitchen?’: Online misogyny, YouTube comments and women's professional street skateboarding

2021 ◽  
pp. 101269022110215
Author(s):  
Brigid McCarthy
Keyword(s):  
Women Athletes ◽  
Well Being ◽  
Gendered Language ◽  
Barriers To Participation ◽  
Specific Research ◽  
Online Platforms ◽  
Global Issue ◽  
Gender Boundaries ◽  
Mental Well Being ◽  
Online Misogyny

Abuse and harassment of sportswomen has become a global issue. And while the sportification of skateboarding has increased professional opportunities and media visibility for women athletes, it has also resulted in misogyny and gendered abuse on online platforms where competition coverage is posted. This study examines comments that collectively target competitors in YouTube streams of major professional women’s street skating competitions. Examined through the lens of ‘virtual manhood acts’, it demonstrates how gender boundaries of skateboarding are policed online through masculine acts such as gendered language, comparison, sexualisation and stigmatisation of non-normative femininities. In undertaking these virtual manhood acts, perpetrators delegitimise women skaters collectively and engage in strategies that elevate male membership in both the sport and fandom. The pervasive presence of abuse and misogyny highlights a need for further sport-specific research into behaviours which may impact athletes’ emotional and mental well-being, and create further barriers to participation, particularly in male-dominated sports cultures.

scholarly journals A review of flash glucose monitoring in type 2 diabetes

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Marcio Krakauer ◽  
Jose Fernando Botero ◽  
Fernando J. Lavalle-González ◽  
Adrian Proietti ◽  
Douglas Eugenio Barbieri
Keyword(s):  
Type 2 Diabetes ◽  
Insulin Therapy ◽  
Real World ◽  
Glucose Monitoring ◽  
Well Being ◽  
Main Body ◽  
Flash Glucose Monitoring ◽  
Glucose Levels ◽  
Freestyle Libre

Abstract Background Continuous glucose monitoring systems are increasingly being adopted as an alternative to self-monitoring of blood glucose (SMBG) by persons with diabetes mellitus receiving insulin therapy. Main body The FreeStyle Libre flash glucose monitoring system (Abbott Diabetes Care, Witney, United Kingdom) consists of a factory-calibrated sensor worn on the back of the arm which measures glucose levels in the interstitial fluid every minute and stores the reading automatically every 15 min. Swiping the reader device over the sensor retrieves stored data and displays current interstitial glucose levels, a glucose trend arrow, and a graph of glucose readings over the preceding 8 h. In patients with type 2 diabetes (T2D) receiving insulin therapy, pivotal efficacy data were provided by the 6-month REPLACE randomized controlled trial (RCT) and 6-month extension study. Compared to SMBG, the flash system significantly reduced the time spent in hypoglycemia and frequency of hypoglycemic events, although no significant change was observed in glycosylated hemoglobin (HbA1c) levels. Subsequent RCTs and real-world chart review studies have since shown that flash glucose monitoring significantly reduces HbA1c from baseline. Real-world studies in both type 1 diabetes or T2D populations also showed that flash glucose monitoring improved glycemic control. Higher (versus lower) scanning frequency was associated with significantly greater reductions in HbA1c and significant improvements in other measures such as time spent in hypoglycemia, time spent in hyperglycemia, and time in range. Additional benefits associated with flash glucose monitoring versus SMBG include reductions in acute diabetes events, all-cause hospitalizations and hospitalized ketoacidosis episodes; improved well-being and decreased disease burden; and greater treatment satisfaction. Conclusion T2D patients who use flash glucose monitoring might expect to achieve significant improvement in HbA1c and glycemic parameters and several associated benefits.

scholarly journals Overlapping Community Detection Based on Attribute Augmented Graph

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 680
Author(s):  
Hanyang Lin ◽  
Yongzhao Zhan ◽  
Zizheng Zhao ◽  
Yuzhong Chen ◽  
Chen Dong
Keyword(s):  
Community Detection ◽  
Real World ◽  
Detection Algorithm ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Adjustment Strategy ◽  
Topology Information ◽  
Overlapping Community ◽  
Real World Datasets ◽  
Community Detection Algorithm

There is a wealth of information in real-world social networks. In addition to the topology information, the vertices or edges of a social network often have attributes, with many of the overlapping vertices belonging to several communities simultaneously. It is challenging to fully utilize the additional attribute information to detect overlapping communities. In this paper, we first propose an overlapping community detection algorithm based on an augmented attribute graph. An improved weight adjustment strategy for attributes is embedded in the algorithm to help detect overlapping communities more accurately. Second, we enhance the algorithm to automatically determine the number of communities by a node-density-based fuzzy k-medoids process. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively detect overlapping communities with fewer parameters compared to the baseline methods.

Review Summary Generation in Online Systems: Frameworks for Supervised and Unsupervised Scenarios

2021 ◽  
Vol 15 (3) ◽  
pp. 1-33
Author(s):  
Wenjun Jiang ◽  
Jing Chen ◽  
Xiaofei Ding ◽  
Jie Wu ◽  
Jiawei He ◽  
...  
Keyword(s):  
Decision Making ◽  
Real World ◽  
Text Summarization ◽  
Experimental Results ◽  
Product Review ◽  
Comprehensive Review ◽  
Online Systems ◽  
Real World Datasets ◽  
Different Characteristics

In online systems, including e-commerce platforms, many users resort to the reviews or comments generated by previous consumers for decision making, while their time is limited to deal with many reviews. Therefore, a review summary, which contains all important features in user-generated reviews, is expected. In this article, we study “how to generate a comprehensive review summary from a large number of user-generated reviews.” This can be implemented by text summarization, which mainly has two types of extractive and abstractive approaches. Both of these approaches can deal with both supervised and unsupervised scenarios, but the former may generate redundant and incoherent summaries, while the latter can avoid redundancy but usually can only deal with short sequences. Moreover, both approaches may neglect the sentiment information. To address the above issues, we propose comprehensive Review Summary Generation frameworks to deal with the supervised and unsupervised scenarios. We design two different preprocess models of re-ranking and selecting to identify the important sentences while keeping users’ sentiment in the original reviews. These sentences can be further used to generate review summaries with text summarization methods. Experimental results in seven real-world datasets (Idebate, Rotten Tomatoes Amazon, Yelp, and three unlabelled product review datasets in Amazon) demonstrate that our work performs well in review summary generation. Moreover, the re-ranking and selecting models show different characteristics.

scholarly journals Improving Gifted Talent Development Can Help Solve Multiple Consequential Real-World Problems

2021 ◽  
Vol 9 (2) ◽  
pp. 31
Author(s):  
Jonathan Wai ◽  
Benjamin J. Lovett
Keyword(s):  
Real World ◽  
Longitudinal Research ◽  
Well Being ◽  
Health Science ◽  
High Achievers ◽  
Developmental Needs ◽  
Gifted Programming ◽  
Cognitive Aptitude ◽  
Fundamental Goal ◽  
Real World Problems

Fully developing the talents of all students is a fundamental goal for personal well-being and development and ultimately for global societal innovation and flourishing. However, in this paper we focus on what we believe is an often neglected and underdeveloped population, that of the gifted. We draw from the cognitive aptitude and gifted education research literatures to make the case that solutions to consequential real-world problems can be greatly enhanced by more fully developing the talents of the intellectually gifted population, which we operationalize in this paper as roughly the top 5% of cognitive talent. Should well-supported high achievers choose to solve them, these problems span health, science, economic growth, and areas unforeseen. We draw from longitudinal research on intellectually precocious students and retrospective research on leaders and innovators in society, showing that mathematical, verbal, and spatial aptitudes are linked to societal innovation. We then discuss two remaining fundamental challenges: the identification of disadvantaged and marginalized groups of students who have traditionally been neglected in selection for gifted programming suited to their current developmental needs, and the building of skills beyond academic ones, specifically in the related areas of open-minded thinking and intellectual humility.

Sign in / Sign up

Export Citation Format

Share Document