Policy Coherence and Coordination for Trade Facilitation: Integrated Border Management, Single-Windows and other Options for Developing Countries

There is now increasing recognition of the critical importance of trade facilitation to further international commerce, accelerate growth, and enhance welfare if not alleviate poverty among trading nations. But there is also increasing appreciation that it is not just attention to the barriers and bottlenecks behind-the-border that are involved in trade facilitation (TF), it also calls for coherence between policies and regulations at the border and inside the border. The unavoidable participation of many government agencies and private stakeholders in border transactions calls for coordination among them towards a harmonized approach to trade facilitation. This paper discusses the need and relevance of policy coherence and coordination to facilitate trade and to what extent some trade facilitation measures (concepts) such as integrated border management and single-windows may be applicable in developing countries to improve both policy coherence and coordination.Trade Faclitation, Border trade, Policy coherence, Economic Integration

Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking

This paper proposes a new neural architecture for collaborative ranking with implicit feedback. Our model, LRML (\textit{Latent Relational Metric Learning}) is a novel metric learning approach for recommendation. More specifically, instead of simple push-pull mechanisms between user and item pairs, we propose to learn latent relations that describe each user item interaction. This helps to alleviate the potential geometric inflexibility of existing metric learing approaches. This enables not only better performance but also a greater extent of modeling capability, allowing our model to scale to a larger number of interactions. In order to do so, we employ a augmented memory module and learn to attend over these memory blocks to construct latent relations. The memory-based attention module is controlled by the user-item interaction, making the learned relation vector specific to each user-item pair. Hence, this can be interpreted as learning an exclusive and optimal relational translation for each user-item interaction. The proposed architecture demonstrates the state-of-the-art performance across multiple recommendation benchmarks. LRML outperforms other metric learning models by $6\%-7.5\%$ in terms of Hits@10 and nDCG@10 on large datasets such as Netflix and MovieLens20M. Moreover, qualitative studies also demonstrate evidence that our proposed model is able to infer and encode explicit sentiment, temporal and attribute information despite being only trained on implicit feedback. As such, this ascertains the ability of LRML to uncover hidden relational structure within implicit datasets.Comment: WWW 201

Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502

Regional Integration and Inclusive Development: Lessons from ASEAN Experience

The current economic crisis has lent extra urgency to ASEAN’s efforts at economic integration and raising its attractiveness for trade and investment. This process gained momentum in the 1990s and has made much progress, as reflected in the emergence of a wide range of extra- and intra-regional agreements. However, the effectiveness of this network of arrangements in stimulating trade and investment depends on not just the characteristics of each arrangement but how well they complement each other. This paper addresses these two areas by examining and evaluating past and present initiatives individually and collectively. Findings suggest that the fundamental impeding issues have endured over the years: lack of political will, ASEAN-style consensus-reliant negotiation, and insufficient management in implementing and harmonising of initiatives. Recommendations include agreement design innovation and focus on shared concerns to overcome lack of will, role expansion of the secretariat to monitor implementation through issuance of score cards, and establishment of specialised bodies such as sub-committees and working groups to enhance implementation and dispute settlement.ASEAN, Economic Integration, Development, Trade