Learning Adaptive Display Exposure for Real-Time Advertising

In E-commerce advertising, where product recommendations and product ads are presented to users simultaneously, the traditional setting is to display ads at fixed positions. However, under such a setting, the advertising system loses the flexibility to control the number and positions of ads, resulting in sub-optimal platform revenue and user experience. Consequently, major e-commerce platforms (e.g., Taobao.com) have begun to consider more flexible ways to display ads. In this paper, we investigate the problem of advertising with adaptive exposure: can we dynamically determine the number and positions of ads for each user visit under certain business constraints so that the platform revenue can be increased? More specifically, we consider two types of constraints: request-level constraint ensures user experience for each user visit, and platform-level constraint controls the overall platform monetization rate. We model this problem as a Constrained Markov Decision Process with per-state constraint (psCMDP) and propose a constrained two-level reinforcement learning approach to decompose the original problem into two relatively independent sub-problems. To accelerate policy learning, we also devise a constrained hindsight experience replay mechanism. Experimental evaluations on industry-scale real-world datasets demonstrate the merits of our approach in both obtaining higher revenue under the constraints and the effectiveness of the constrained hindsight experience replay mechanism.Comment: accepted by CIKM201

Learning List-wise Representation in Reinforcement Learning for Ads Allocation with Multiple Auxiliary Tasks

With the recent prevalence of reinforcement learning (RL), there have been tremendous interests in utilizing RL for ads allocation in recommendation platforms (e.g., e-commerce and news feed sites). For better performance, recent RL-based ads allocation agent makes decisions based on representations of list-wise item arrangement. This results in a high-dimensional state-action space, which makes it difficult to learn an efficient and generalizable list-wise representation. To address this problem, we propose a novel algorithm to learn a better representation by leveraging task-specific signals on Meituan food delivery platform. Specifically, we propose three different types of auxiliary tasks that are based on reconstruction, prediction, and contrastive learning respectively. We conduct extensive offline experiments on the effectiveness of these auxiliary tasks and test our method on real-world food delivery platform. The experimental results show that our method can learn better list-wise representations and achieve higher revenue for the platform.Comment: arXiv admin note: text overlap with arXiv:2109.04353, arXiv:2204.0037

Personalized Adaptive Meta Learning for Cold-start User Preference Prediction

A common challenge in personalized user preference prediction is the cold-start problem. Due to the lack of user-item interactions, directly learning from the new users' log data causes serious over-fitting problem. Recently, many existing studies regard the cold-start personalized preference prediction as a few-shot learning problem, where each user is the task and recommended items are the classes, and the gradient-based meta learning method (MAML) is leveraged to address this challenge. However, in real-world application, the users are not uniformly distributed (i.e., different users may have different browsing history, recommended items, and user profiles. We define the major users as the users in the groups with large numbers of users sharing similar user information, and other users are the minor users), existing MAML approaches tend to fit the major users and ignore the minor users. To address this cold-start task-overfitting problem, we propose a novel personalized adaptive meta learning approach to consider both the major and the minor users with three key contributions: 1) We are the first to present a personalized adaptive learning rate meta-learning approach to improve the performance of MAML by focusing on both the major and minor users. 2) To provide better personalized learning rates for each user, we introduce a similarity-based method to find similar users as a reference and a tree-based method to store users' features for fast search. 3) To reduce the memory usage, we design a memory agnostic regularizer to further reduce the space complexity to constant while maintain the performance. Experiments on MovieLens, BookCrossing, and real-world production datasets reveal that our method outperforms the state-of-the-art methods dramatically for both the minor and major users.Comment: Preprint Versio

Taxation in the Digital Economy

A robust and efficient tax administration in a modern tax system requires effective tax policies and legislation. Policy frameworks should cover all aspects of tax administration and include the essential processes of capturing, processing, analyzing, and responding to information provided by taxpayers and others concerning taxpayers’ affairs. By far the greatest challenges facing tax administrations in all countries are those posed by the continuing developments in the digital economy. Whereas societies are grappling to come to terms with the transitions from the third industrial or digital revolutions, revenue authorities grapple with the consequences for the sustainability of their tax bases and the efficient administration and collection of taxes. This book presents a critical review of the status of tax systems in Asia and the Pacific in the era of the digital economy. The book suggests how countries can maximize their domestic resource mobilization when confronted by the challenges that digitalization inevitably produces, as well as how they can best harness or take advantage of aspects of digitalization to serve their own needs. The full implications of the COVID-19 crisis are still too uncertain to predict, but it is clear that the crisis will accelerate the trend towards digitalization and also increase pressures on public finances. This, in turn, may shape the preference for, and the nature of, both multilateral and unilateral responses to the tax challenges posed by digitalization and the need to address them. This book will be a timely reference for those researching on taxation in digital economy and for policy makers.

Characterisation framework of key policy, regulatory and governance dynamics and impacts upon European food value chains: Fairer trading practices, food integrity, and sustainability collaborations. : VALUMICS project “Understanding Food Value Chains and Network Dynamics” funded by EU Horizon 2020 G.A. No 727243. Deliverable D3.3

The report provides a framework that categorises the different European Union (EU) policies, laws and governance actions identified as impacting upon food value chains in the defined areas of: fairer trading practices, food integrity (food safety and authenticity), and sustainability collaborations along food value chains. A four-stage framework is presented and illustrated with examples. The evidence shows that European Union policy activity impacting upon food value chain dynamics is increasing, both in terms of the impacts of policies upon the chains, and, in terms of addressing some of the more contentious outcomes of these dynamics. A number of policy priorities are at play in addressing the outcomes of food value chain dynamics. unevenness of the distribution of profit within food value chains, notably to farmers. Regulation of food safety and aspects of authenticity has been a key focus for two decades to ensure a functioning single market while ensuring consumer health and wellbeing. A food chain length perspective has been attempted, notably through regulations such as the General Food Law, and the rationalisation of the Official Controls on food and feed safety. However, there are still gaps in the effective monitoring and transparency of food safety and of food integrity along value chains, as exemplified by misleading claims and criminal fraud. This has led to renewed policy actions over food fraud, in particular. EU regulations, policies and related governance initiatives provide an important framework for national-level actions for EU member states and for EEA members. The more tightly EU-regulated areas, such as food safety, see fewer extra initiatives, but where there is a more general strategic policy and governance push, such as food waste reduction or food fraud, there is greater independent state-level activity. Likewise, there is much more variation in the application of both national and European (Competition) law to govern unfair trading practices impacting upon food value chains. This report presents the findings of a survey of members from the VALUMICS stakeholder platform, that were policy facing food value chain stakeholders across selected European countries, including both EU and EEA Member States. The survey was conducted to check the significance of the main policies identified in the mapping exercise at EU and national levels and so to incorporate the views of stakeholders in the research. The responses suggest the policy concerns identified in EU and national-level research resonate with food value chain stakeholders in participating nations. The report concludes by exploring in more detail how the themes of fairness and of transparency are being handled in the policy activities presented. Highlighted are the ways that both fairness and transparency can be extended within the existing frameworks of EU policy activity. The findings in this report provide an important context for further and detailed research analysis of the workings and dynamics of European food value chains under the VALUMICS project

Combating Fiscal Fraud and Empowering Regulators

This book showcases a multidisciplinary set of work on the impact of regulatory innovation on the scale and nature of tax evasion, tax avoidance, and money laundering. We consider the international tax environment an ecosystem undergoing a period of rapid change as shocks such as the financial crisis, new business forms, scandals and novel regulatory instruments impact upon it. This ecosystem evolves as jurisdictions, taxpayers, and experts react. Our analysis focuses mainly on Europe and five new regulations: Automatic Exchange of Information, which requires that accounts held by foreigners are reported to authorities in the account holder’s country of residence; the OECD’s Base Erosion and Profit Shifting initiative and Country by Country Reporting, which attempt to reduce the opportunity spaces in which corporations can limit tax payments and utilize low or no tax jurisdictions; the Legal Entity Identifier which provides a 20-digit identification code for all individual, corporate or government entities conducting financial transactions; and the Fourth and Fifth Anti-Money Laundering Directives, that criminalize tax crimes and prescribe that the Ultimate Beneficial Owner of a company is registered. Working from accounting, economic, political science, and legal perspectives, the analysis in this book provides an assessment of the reforms and policy recommendations that will reinforce the international tax system. The collection also flags the dangers posed by emerging tax loopholes provided by new business models and in the form of freeports and golden passports. Our central message is that inequality can and has to be reduced substantially, and we can achieve this through an improved international tax system

Resolving corporate bribery through deferred prosecution agreements:Lessons from the US, UK and France for China

While bribery is designated as a criminal offense in most jurisdictions, the enforcement of anti-bribery laws in the corporate context is far from satisfactory. The weak enforcement can be mainly attributed to the practical challenges of doing so. Benefiting from deferred prosecution agreements (DPAs), the U.S., UK and French authorities have significantly ramped up their anti-bribery enforcement and encouraged corporate self-policing activities. Inspired by the foreign DPA developments, China’s prosecutorial authorities have been actively promoting the compliance non-prosecution program (CNP) since 2020. Introduced amid the Covid-19 pandemic and the ever-intensive U.S.-China trade conflicts, the CNP aims to mitigate the adverse economic implications of corporate criminal enforcement and foster corporate compliance.Combining legal doctrinal research, comparative research and insights from the law and economics literature, this thesis provides an overview of the DPA regimes in the U.S., UK and France and the CNP in China. It analyzes the advantages and weakness of the DPA programs in the three jurisdictions, aiming to draw lessons for developing the Chinese version of DPA program to address corporate bribery. Meanwhile, it also identifies the reasons for the inactive role played by the corporations in China’s anti-bribery movement and the challenges caused for the authorities in the anti-bribery enforcement. It is proposed that a Chinese version of DPA program be established based on the existing CNP to resolve corporate bribery cases. When designing and applying the Chinese version of DPA program and complementary regimes, special attention should be paid to deterrence, rehabilitation, and individual accountability.<br/

Resolving corporate bribery through deferred prosecution agreements:Lessons from the US, UK and France for China

While bribery is designated as a criminal offense in most jurisdictions, the enforcement of anti-bribery laws in the corporate context is far from satisfactory. The weak enforcement can be mainly attributed to the practical challenges of doing so. Benefiting from deferred prosecution agreements (DPAs), the U.S., UK and French authorities have significantly ramped up their anti-bribery enforcement and encouraged corporate self-policing activities. Inspired by the foreign DPA developments, China’s prosecutorial authorities have been actively promoting the compliance non-prosecution program (CNP) since 2020. Introduced amid the Covid-19 pandemic and the ever-intensive U.S.-China trade conflicts, the CNP aims to mitigate the adverse economic implications of corporate criminal enforcement and foster corporate compliance.Combining legal doctrinal research, comparative research and insights from the law and economics literature, this thesis provides an overview of the DPA regimes in the U.S., UK and France and the CNP in China. It analyzes the advantages and weakness of the DPA programs in the three jurisdictions, aiming to draw lessons for developing the Chinese version of DPA program to address corporate bribery. Meanwhile, it also identifies the reasons for the inactive role played by the corporations in China’s anti-bribery movement and the challenges caused for the authorities in the anti-bribery enforcement. It is proposed that a Chinese version of DPA program be established based on the existing CNP to resolve corporate bribery cases. When designing and applying the Chinese version of DPA program and complementary regimes, special attention should be paid to deterrence, rehabilitation, and individual accountability.<br/