Understanding and Mitigating Multi-sided Exposure Bias in Recommender Systems

Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. It is especially important in multi-sided recommendation platforms where it may be crucial to optimize utilities not just for the end user, but also for other actors such as item sellers or producers who desire a fair representation of their items. Existing solutions do not properly address various aspects of multi-sided fairness in recommendations as they may either solely have one-sided view (i.e. improving the fairness only for one side), or do not appropriately measure the fairness for each actor involved in the system. In this thesis, I aim at first investigating the impact of unfair recommendations on the system and how these unfair recommendations can negatively affect major actors in the system. Then, I seek to propose solutions to tackle the unfairness of recommendations. I propose a rating transformation technique that works as a pre-processing step before building the recommendation model to alleviate the inherent popularity bias in the input data and consequently to mitigate the exposure unfairness for items and suppliers in the recommendation lists. Also, as another solution, I propose a general graph-based solution that works as a post-processing approach after recommendation generation for mitigating the multi-sided exposure bias in the recommendation results. For evaluation, I introduce several metrics for measuring the exposure fairness for items and suppliers, and show that these metrics better capture the fairness properties in the recommendation results. I perform extensive experiments to evaluate the effectiveness of the proposed solutions. The experiments on different publicly-available datasets and comparison with various baselines confirm the superiority of the proposed solutions in improving the exposure fairness for items and suppliers.Comment: Doctoral thesi

Detail design, building and commissioning of tall building structural models for experimental shaking table tests

Copyright © 2015 John Wiley & Sons, Ltd. Summary In the areas of seismic engineering, shaking table tests are powerful methods for assessing the seismic capacity of buildings. Since the size and capacity of existing shaking tables are limited, using scale structural models seems to be necessary. In recent years, many experimental studies have been performed using shaking table tests to determine seismic response of structural models subjected to various earthquake records. However, none of the past research works discussed practical procedure for creating the physical model. Therefore, in this study, a comprehensive procedure for design, building and commissioning of scale tall building structural models has been developed and presented for practical applications in shaking table test programmes. To validate the structural model, shaking table tests and numerical time history dynamic analyses were performed under the influence of different scaled earthquake acceleration records. Comparing the numerical predictions and experimental values of maximum lateral displacements, it became apparent that the numerical predictions and laboratory measurements are in a good agreement. As a result, the scale structural model can replicate the behaviour of real tall buildings with acceptable accuracy. It is concluded that the physical model is a valid and qualified model that can be employed for experimental shaking table tests

Firm size, audit regulation and fraud detection

Revizor je odgovoren za preprečevanje, odkrivanje in poročanje o goljufijah. Pri revidiranju so najspornejši nezakoniti akti in napake. Ti področji sta tudi najpogostejši predmet razprave med revizorji, politiki, mediji, zakonodajalci in javnostjo (Gaz in dr. 1997). S predhodno raziskavo je bila ugotovljena pozitivna povezanost med kakovostjo revizije ter velikostjo revizorskega podjetja. Medtem ko so nekatere študije kot nadomestilo za kakovost revidiranja uporabile revizijske stroške, so druge študije uporabile bolj neposredna merila, kot so rezultati poročil o nadzoru kakovosti. Vendar so slednje študije uporabljale vzorce, ki so močno geografsko omejeni ali pa so omejeni v smislu vrste naročnika. To še ni vse. Večina raziskav o povezanosti med velikostjo in kakovostjo se je tudi osredotočila na dokaj velika pooblaščena revizijska (CPA) podjetja. Zadnja leta se je veliko razpravljalo o naravi revizijske prakse (Salehi 2007). Revizorji so odgovorni tudi za zagotavljanje točnosti in natančnosti izjav, ki jih pripravijo managerji

Fairness of Exposure in Dynamic Recommendation

Exposure bias is a well-known issue in recommender systems where the exposure is not fairly distributed among items in the recommendation results. This is especially problematic when bias is amplified over time as a few items (e.g., popular ones) are repeatedly over-represented in recommendation lists and users' interactions with those items will amplify bias towards those items over time resulting in a feedback loop. This issue has been extensively studied in the literature in static recommendation environment where a single round of recommendation result is processed to improve the exposure fairness. However, less work has been done on addressing exposure bias in a dynamic recommendation setting where the system is operating over time, the recommendation model and the input data are dynamically updated with ongoing user feedback on recommended items at each round. In this paper, we study exposure bias in a dynamic recommendation setting. Our goal is to show that existing bias mitigation methods that are designed to operate in a static recommendation setting are unable to satisfy fairness of exposure for items in long run. In particular, we empirically study one of these methods and show that repeatedly applying this method fails to fairly distribute exposure among items in long run. To address this limitation, we show how this method can be adapted to effectively operate in a dynamic recommendation setting and achieve exposure fairness for items in long run. Experiments on a real-world dataset confirm that our solution is superior in achieving long-term exposure fairness for the items while maintaining the recommendation accuracy

Kaiy (traditional cautery) in Benghazi, Libya: complications versus effectiveness

Introduction: The practice of Kaiy (Cautery) as a traditional therapy is not science based though it is widely practiced worldwide. In Libya, in particular, it is commonly used with no any report or publication to emphasis on its positive or negative impact. This work was undertaken to highlight the complications and disadvantages of kaiy in the Libyan societies as it seems to cause more harm than benefit for the patient. Methods: We conducted a questionnaire-based survey in the period from the first of March to the end of April (two months) of the year 2013, on fifty patients who were collected from different hospitals in Benghazi city,  and who had experienced Kaiy therapy for different diseases. Results: We found that kaiy application is more common among non educated patients (30 patients, 60%). Most of patients (45 cases, 90%) followed their relatives' advice and that 32 cases (63.5%) did not improve and show undesirable manifestations and complications. Conclusion: This study has shown that Kaiy therapy is associated with considerable health risks; therefore, we recommend discouraging and restricting its application.Key words: Burning, complications, counter irritant, health car

Firm Size and Audit Regulation and Fraud Detection: Empirical Evidence from Iran

An auditor has the responsibility for the prevention, detection and reporting of fraud, other illegal acts and errors is one of the most controversial issues in auditing, and has been one of the most frequently debated areas amongst auditors, politicians, media, regulators and the public (Gay et al 1997). Prior research has documented a positive association between audit quality and auditor size. While some studies have used audit fee as a surrogate for audit quality, other studies have employed more direct measures, such as the outcomes of quality control reviews. Those latter studies, however, used samples that suffer from severe geographic or client type restrictions. Moreover, most studies of the quality-size relationship have focused on relatively large CPA firms. In recent years there has been considerable debate about the nature of audit practice (Salehi, 2007). Auditors also have responsibility regarding accuracy and precise of statements prepared by managers

Career Path Recommendations for Long-term Income Maximization: A Reinforcement Learning Approach

This study explores the potential of reinforcement learning algorithms to enhance career planning processes. Leveraging data from Randstad The Netherlands, the study simulates the Dutch job market and develops strategies to optimize employees' long-term income. By formulating career planning as a Markov Decision Process (MDP) and utilizing machine learning algorithms such as Sarsa, Q-Learning, and A2C, we learn optimal policies that recommend career paths with high-income occupations and industries. The results demonstrate significant improvements in employees' income trajectories, with RL models, particularly Q-Learning and Sarsa, achieving an average increase of 5% compared to observed career paths. The study acknowledges limitations, including narrow job filtering, simplifications in the environment formulation, and assumptions regarding employment continuity and zero application costs. Future research can explore additional objectives beyond income optimization and address these limitations to further enhance career planning processes.Comment: accepted for publication at RecSys in HR '23 (at the 17th ACM Conference on Recommender Systems