ENHANCE NMF-BASED RECOMMENDATION SYSTEMS WITH AUXILIARY INFORMATION IMPUTATION

This dissertation studies the factors that negatively impact the accuracy of the collaborative filtering recommendation systems based on nonnegative matrix factorization (NMF). The keystone in the recommendation system is the rating that expresses the user\u27s opinion about an item. One of the most significant issues in the recommendation systems is the lack of ratings. This issue is called cold-start issue, which appears clearly with New-Users who did not rate any item and New-Items, which did not receive any rating. The traditional recommendation systems assume that users are independent and identically distributed and ignore the connections among users whereas the recommendation actually is a social activity. This dissertation aims to enhance NMF-based recommendation systems by utilizing the imputation method and limiting the errors that are introduced in the system. External information such as trust network and item categories are incorporated into NMF-based recommendation systems through the imputation. The proposed approaches impute various subsets of the missing ratings. The subsets are defined based on the total number of the ratings of the user or item before the imputation, such as impute the missing ratings of New-Users, New-Items, or cold-start users or items that suffer from the lack of the ratings. In addition, several factors are analyzed that affect the prediction accuracy when the imputation method is utilized with NMF-based recommendation systems. These factors include the total number of the ratings of the user or item before the imputation, the total number of imputed ratings for each user and item, the average of imputed rating values, and the value of imputed rating values. In addition, several strategies are applied to select the subset of missing ratings for the imputation that lead to increasing the prediction accuracy and limiting the imputation error. Moreover, a comparison is conducted with some popular methods that are in common with the proposed method in utilizing the imputation to handle the lack of ratings, but they differ in the source of the imputed ratings. Experiments on different large-size datasets are conducted to examine the proposed approaches and analyze the effects of the imputation on accuracy. Users and items are divided into three groups based on the total number of the ratings before the imputation is applied and their recommendation accuracy is calculated. The results show that the imputation enhances the recommendation system by capacitating the system to recommend items to New-Users, introduce New-Items to users, and increase the accuracy of the cold-start users and items. However, the analyzed factors play important roles in the recommendation accuracy and limit the error that is introduced from the imputation

Financial system inquiry: final report

Executive summary This report responds to the objective in the Inquiry’s Terms of Reference to best position Australia’s financial system to meet Australia’s evolving needs and support economic growth. It offers a blueprint for an efficient and resilient financial system over the next 10 to 20 years, characterised by the fair treatment of users.   The Inquiry has made 44 recommendations relating to the Australian financial system. These recommendations reflect the Inquiry’s judgement and are based on evidence received by the Inquiry. The Inquiry’s test has been one of public interest: the interests of individuals, businesses, the economy, taxpayers and Government.   Australia’s financial system has performed well since the Wallis Inquiry and has many strong characteristics. It also has a number of weaknesses: taxation and regulatory settings distort the flow of funding to the real economy; it remains susceptible to financial shocks; superannuation is not delivering retirement incomes efficiently; unfair consumer outcomes remain prevalent; and policy settings do not focus on the benefits of competition and innovation. As a result, the system is prone to calls for more regulation.   To put these issues in context, the Overview first deals with the characteristics of Australia’s economy. It then describes the characteristics of and prerequisites for a well-functioning financial system and the Inquiry’s philosophy of financial regulation.   The Inquiry focuses on seven themes in this report (summarised in Guide to the Financial System Inquiry Final Report).   The Overview deals with the general themes of funding the Australian economy and competition.   The Inquiry has also made recommendations on five specific themes, which comprise the next chapters of this report: Strengthen the economy by making the financial system more resilient. Lift the value of the superannuation system and retirement incomes. Drive economic growth and productivity through settings that promote innovation. Enhance confidence and trust by creating an environment in which financial firms treat customers fairly. Enhance regulator independence and accountability and minimise the need for future regulation. These recommendations seek to improve efficiency, resilience and fair treatment in the Australian financial system, allowing it to achieve its potential in supporting economic growth and enhancing standards of living for current and future generations.   Financial system inquiry committee   Mr David Murray AO (Chair) Mr David Murray AO (Sydney) was most recently the inaugural Chairman of the Australian Government’s Future Fund Board of Guardians between 2006 and 2012. Mr Murray was previously the Chief Executive Officer of the Commonwealth Bank of Australia between 1992 and 2005. In this time, Mr. Murray oversaw the transformation of the Commonwealth Bank from a partly privatised bank to an integrated financial services company. In 2001, he was awarded the Centenary Medal for service to Australian society in banking and corporate governance, and in 2007 he was made an Officer of the Order of Australia for his service to the finance sector, both domestically and globally, and service to the community.   Professor Kevin Davis Professor Kevin Davis (Melbourne) is currently a Professor of Finance at the University of Melbourne, Research Director at the Australian Centre for Financial Studies and a Professor of Finance at Monash University. Professor Davis is also a part-time member of the Australian Competition Tribunal and Co-Chair of the Australia–New Zealand Shadow Financial Regulatory Committee.   Mr Craig Dunn Mr Craig Dunn (Sydney) was most recently Chief Executive Officer and Managing Director of AMP. Mr Dunn led AMP through the global financial crisis and has extensive experience in the financial sector. He was a member of the Australian Government\u27s Financial Sector Advisory Council and the Australian Financial Centre Forum, and an executive member of the Australia Japan Business Co-operation Committee. Mr Dunn is a director of the Australian Government’s Financial Literacy Board.   Ms Carolyn Hewson AO Ms Carolyn Hewson AO (Adelaide) served as an investment banker at Schroders Australia for 15 years. Ms Hewson has over 30 years’ experience in the finance sector and currently serves on the boards of BHP Billiton Ltd and Stockland. Ms Hewson was made an Officer of the Order of Australia for her services to the YWCA and to business. Ms Hewson has served on both the boards of Westpac and AMP and retired from the board of BT Investment Management Ltd and as the Chair of the Westpac Foundation upon her appointment to the Financial System Inquiry Committee.   Dr Brian McNamee AO Dr Brian McNamee AO (Melbourne) served as the Chief Executive Officer and Managing Director of CSL Limited from 1990 to 30 June 2013. During that time, CSL transitioned from a Government-owned enterprise to a global company with a market capitalisation of approximately $30 billion. He has extensive experience in the biotech and global healthcare industries. Dr McNamee was made an Officer of the Order of Australia for his service to business and commerce. &nbsp

Dealing with missing standard deviation and mean values in meta-analysis of continuous outcomes: a systematic review

Background: Rigorous, informative meta-analyses rely on availability of appropriate summary statistics or individual participant data. For continuous outcomes, especially those with naturally skewed distributions, summary information on the mean or variability often goes unreported. While full reporting of original trial data is the ideal, we sought to identify methods for handling unreported mean or variability summary statistics in meta-analysis. Methods: We undertook two systematic literature reviews to identify methodological approaches used to deal with missing mean or variability summary statistics. Five electronic databases were searched, in addition to the Cochrane Colloquium abstract books and the Cochrane Statistics Methods Group mailing list archive. We also conducted cited reference searching and emailed topic experts to identify recent methodological developments. Details recorded included the description of the method, the information required to implement the method, any underlying assumptions and whether the method could be readily applied in standard statistical software. We provided a summary description of the methods identified, illustrating selected methods in example meta-analysis scenarios. Results: For missing standard deviations (SDs), following screening of 503 articles, fifteen methods were identified in addition to those reported in a previous review. These included Bayesian hierarchical modelling at the meta-analysis level; summary statistic level imputation based on observed SD values from other trials in the meta-analysis; a practical approximation based on the range; and algebraic estimation of the SD based on other summary statistics. Following screening of 1124 articles for methods estimating the mean, one approximate Bayesian computation approach and three papers based on alternative summary statistics were identified. Illustrative meta-analyses showed that when replacing a missing SD the approximation using the range minimised loss of precision and generally performed better than omitting trials. When estimating missing means, a formula using the median, lower quartile and upper quartile performed best in preserving the precision of the meta-analysis findings, although in some scenarios, omitting trials gave superior results. Conclusions: Methods based on summary statistics (minimum, maximum, lower quartile, upper quartile, median) reported in the literature facilitate more comprehensive inclusion of randomised controlled trials with missing mean or variability summary statistics within meta-analyses

Data augmentation for recommender system: A semi-supervised approach using maximum margin matrix factorization

Collaborative filtering (CF) has become a popular method for developing recommender systems (RS) where ratings of a user for new items is predicted based on her past preferences and available preference information of other users. Despite the popularity of CF-based methods, their performance is often greatly limited by the sparsity of observed entries. In this study, we explore the data augmentation and refinement aspects of Maximum Margin Matrix Factorization (MMMF), a widely accepted CF technique for the rating predictions, which have not been investigated before. We exploit the inherent characteristics of CF algorithms to assess the confidence level of individual ratings and propose a semi-supervised approach for rating augmentation based on self-training. We hypothesize that any CF algorithm's predictions with low confidence are due to some deficiency in the training data and hence, the performance of the algorithm can be improved by adopting a systematic data augmentation strategy. We iteratively use some of the ratings predicted with high confidence to augment the training data and remove low-confidence entries through a refinement process. By repeating this process, the system learns to improve prediction accuracy. Our method is experimentally evaluated on several state-of-the-art CF algorithms and leads to informative rating augmentation, improving the performance of the baseline approaches.Comment: 20 page

Privacy-Preserving Crowdsourcing-Based Recommender Systems for E-Commerce & Health Services

En l’actualitat, els sistemes de recomanació han esdevingut un mecanisme fonamental per proporcionar als usuaris informació útil i filtrada, amb l’objectiu d’optimitzar la presa de decisions, com per exemple, en el camp del comerç electrònic. La quantitat de dades existent a Internet és tan extensa que els usuaris necessiten sistemes automàtics per ajudar-los a distingir entre informació valuosa i soroll. No obstant, sistemes de recomanació com el Filtratge Col·laboratiu tenen diverses limitacions, com ara la manca de resposta i la privadesa. Una part important d'aquesta tesi es dedica al desenvolupament de metodologies per fer front a aquestes limitacions. A més de les aportacions anteriors, en aquesta tesi també ens centrem en el procés d'urbanització que s'està produint a tot el món i en la necessitat de crear ciutats més sostenibles i habitables. En aquest context, ens proposem solucions de salut intel·ligent (s-health) i metodologies eficients de caracterització de canals sense fils, per tal de proporcionar assistència sanitària sostenible en el context de les ciutats intel·ligents.En la actualidad, los sistemas de recomendación se han convertido en una herramienta indispensable para proporcionar a los usuarios información útil y filtrada, con el objetivo de optimizar la toma de decisiones en una gran variedad de contextos. La cantidad de datos existente en Internet es tan extensa que los usuarios necesitan sistemas automáticos para ayudarles a distinguir entre información valiosa y ruido. Sin embargo, sistemas de recomendación como el Filtrado Colaborativo tienen varias limitaciones, tales como la falta de respuesta y la privacidad. Una parte importante de esta tesis se dedica al desarrollo de metodologías para hacer frente a esas limitaciones. Además de las aportaciones anteriores, en esta tesis también nos centramos en el proceso de urbanización que está teniendo lugar en todo el mundo y en la necesidad de crear ciudades más sostenibles y habitables. En este contexto, proponemos soluciones de salud inteligente (s-health) y metodologías eficientes de caracterización de canales inalámbricos, con el fin de proporcionar asistencia sanitaria sostenible en el contexto de las ciudades inteligentes.Our society lives an age where the eagerness for information has resulted in problems such as infobesity, especially after the arrival of Web 2.0. In this context, automatic systems such as recommenders are increasing their relevance, since they help to distinguish noise from useful information. However, recommender systems such as Collaborative Filtering have several limitations such as non-response and privacy. An important part of this thesis is devoted to the development of methodologies to cope with these limitations. In addition to the previously stated research topics, in this dissertation we also focus in the worldwide process of urbanisation that is taking place and the need for more sustainable and liveable cities. In this context, we focus on smart health solutions and efficient wireless channel characterisation methodologies, in order to provide sustainable healthcare in the context of smart cities

Maximizing Health or Sufficient Capability in Economic Evaluation? A Methodological Experiment of Treatment for Drug Addiction

Conventional practice within the United Kingdom and beyond is to conduct economic evaluations with "health" as evaluative space and "health maximization" as the decision-making rule. However, there is increasing recognition that this evaluative framework may not always be appropriate, and this is particularly the case within public health and social care contexts. This article presents a methodological case study designed to explore the impact of changing the evaluative space within an economic evaluation from health to capability well-being and the decision-making rule from health maximization to the maximization of sufficient capability. Capability well-being is an evaluative space grounded on Amartya Sen's capability approach and assesses well-being based on individuals' ability to do and be the things they value in life. Sufficient capability is an egalitarian approach to decision making that aims to ensure everyone in society achieves a normatively sufficient level of capability well-being. The case study is treatment for drug addiction, and the cost-effectiveness of 2 psychological interventions relative to usual care is assessed using data from a pilot trial. Analyses are undertaken from a health care and a government perspective. For the purpose of the study, quality-adjusted life years (measured using the EQ-5D-5L) and years of full capability equivalent and years of sufficient capability equivalent (both measured using the ICECAP-A [ICEpop CAPability measure for Adults]) are estimated. The study concludes that different evaluative spaces and decision-making rules have the potential to offer opposing treatment recommendations. The implications for policy makers are discussed

Outsourced Congress: How Congress Relies on Outside Organizational Policy Information

In recent decades, in-house policy experience and expertise within Congress has fallen as members of Congress have shifted resources towards constituent-casework, communications and leadership functions and away from personal office or committee policy staff. Headcounts in the legislative support agencies at Congress's disposal have shrunk by over 40 percent since 1979. At the same time, American politics has seen an explosion of activity by policy demanding groups, and privately funded policy research and planning organizations. These organizations are able to serve as auxiliary service bureaus to staffers and members of Congress, strategically providing legislative subsidy in the hopes of affecting policy outcomes. In this dissertation, I develop a micro-level theory of information processing in Congress, in which individual congressional staffers serve as agents of members tasked with the challenge of learning about policy issues and making recommendations to their bosses in complex information environments. It is these individual staffers, I argue, that mediate the institution's need for policy relevant information and these potential sources of outside subsidy. Though dedicated public servants, congressional staffers are generally under-resourced, over-stretched, and frequently on the losing end of an information asymmetry with the policy-demanders that they meet and interests they must rely on for legislative subsidy. As a result, staffers serve less as policy or subject matter experts in their own right, and more as gatekeepers or selective aggregators, engaged in a process of search and evaluation of policy expertise produced by outside interests. The implication of this theory is that members of Congress rely on biased sets of information produced by outside, often ideological interests, and selected for them by constrained and bias-prone staffers. Using original survey data from the 2017 and 2019 Congressional Capacity Surveys, comprising the largest academic survey sample of congressional staff gathered to date, I investigate how congressional staff evaluate privately provisioned, outside policy information depending on the ideological nature of the information source. This work highlights the importance of these ideological networks of outside information purveyors. Finally, I use IRS 990 data from Washington D.C.-based think tanks to map the network of coordination between these subsidy providing organizations that is implied by their interlocking directorates. This dissertation contributes to a broader understanding of Congress by presenting and testing a micro-level theory of information evaluation within the institution, highlighting the importance of individual staffers and their motivations in the collective functioning of the institution. In doing so, it offers a theoretical bridge between scholars of the political organizations that produce these subsidies, and the scholars of Congress, as an institution which relies on them.PHDPolitical ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163019/1/zfurnas_1.pd

The German Socio-Economic Panel Study (SOEP): Scope, Evolution and Enhancements

After the introduction in Section 2, we very briefly sketch out current theoretical and empirical developments in the social sciences. In our view, they all point in the same direction: toward the acute and increasing need for multidisciplinary longitudinal data covering a wide range of living conditions and based on a multitude of variables from the social sciences for both theoretical investigation and the evaluation of policy measures. Cohort and panel studies are therefore called upon to become truly interdisciplinary tools. In Section 3, we describe the German Socio-Economic Panel Study (SOEP), in which we discuss recent improvements of that study which approach this ideal and point out existing shortcomings. Section 4 concludes with a discussion of potential future issues and developments for SOEP and other household panel studies.SOEP, household panel studies, survey design