EU-ASEAN relations in the post-Cold War era:an analysis of the impact of normative factors on interregionalism

This thesis examines the relationship between the European Union (EU) and the Association of Southeast Asian Nations (ASEAN) with a focus on why their normative elements, e.g. values and norms, affect their ties in the post-Cold War era. Since the end of the Cold War, policy-makers and academics have become interested in region-to-region interaction, termed interregionalism. Though interregionalism is considered to have become an indelible feature of post-Cold War international politics, there are question marks over its importance. It is often argued that interregionalism reinforces the collective identity of the regional organisations involved. It is also maintained that its overall relevance to the international system depends on the level of actorness, which is primarily measured in institutional and material terms, of the participant regional organisations. This thesis contends that the normative components of the EU and ASEAN are also fundamental constituents of their actorness and, consequently, define significantly their interregionalism. This is based on a crucial observation that normative factors are of importance to the regional and international relations of the EU and ASEAN. Yet, while they strongly espouse norms and values to guide their internal and external activities, their normative premises radically differ from each other. Furthermore, these normative differences jeopardise their cooperation. Building on this observation the inquiry takes the normative components of the EU and ASEAN as the criterion as well as the focus for investigating their interregionalism. In doing so, it hypothesises that the EU and ASEAN are two different regional actors that adopt two dissimilar sets of norms to conduct their regional and international affairs and that such normative differences hinder their relations. Within this hypothesis, it seeks to address three central questions. First, what are the normative features that constitute the EU and ASEAN as actors in world politics and that make them different from each other? Second, what are the main sources of their normative differences? Finally, why do their normative differences become an obstructive factor in their relationship? To address these issues, the inquiry adopts a constructivist interpretation (of International Relations) and opts for a narrative and empirical inquiry, which is based on information and data acquired from official documents, scholarly works and interviews and questionnaires. In doing so, it finds that as they were born and evolved in two dissimilar temporal and spatial settings, the EU and ASEAN are two different norm entrepreneurs and normative powers. The former advocates a set of liberal cosmopolitan norms whereas the latter champions a set of traditional communitarian principles. Their normative differences become a major obstacle to their cooperation, especially when one regional organisation’s norms are refused or violated by the other. Thus, a key lesson drawn from these findings is that in order to explain more fully EU-ASEAN interregionalism, it is essential to consider their norms, the reasons behind their normative differences and the implication of those differences to their relations

Connecting social media to e-commerce:cold-start product recommendation using microblogging information

In recent years, the boundaries between e-commerce and social networking have become increasingly blurred. Many e-commerce websites support the mechanism of social login where users can sign on the websites using their social network identities such as their Facebook or Twitter accounts. Users can also post their newly purchased products on microblogs with links to the e-commerce product web pages. In this paper, we propose a novel solution for cross-site cold-start product recommendation, which aims to recommend products from e-commerce websites to users at social networking sites in 'cold-start' situations, a problem which has rarely been explored before. A major challenge is how to leverage knowledge extracted from social networking sites for cross-site cold-start product recommendation. We propose to use the linked users across social networking sites and e-commerce websites (users who have social networking accounts and have made purchases on e-commerce websites) as a bridge to map users' social networking features to another feature representation for product recommendation. In specific, we propose learning both users' and products' feature representations (called user embeddings and product embeddings, respectively) from data collected from e-commerce websites using recurrent neural networks and then apply a modified gradient boosting trees method to transform users' social networking features into user embeddings. We then develop a feature-based matrix factorization approach which can leverage the learnt user embeddings for cold-start product recommendation. Experimental results on a large dataset constructed from the largest Chinese microblogging service Sina Weibo and the largest Chinese B2C e-commerce website JingDong have shown the effectiveness of our proposed framework.

Collaborative filtering and deep learning based recommendation system for cold start items

Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.

Collaborative filtering and deep learning based hybrid recommendation for cold start problem

Recommender systems (RS) are used by many social networking applications and online e-commercial services. Collaborative filtering (CF) is one of the most popular approaches used for RS. However traditional CF approach suffers from sparsity and cold start problems. In this paper, we propose a hybrid recommendation model to address the cold start problem, which explores the item content features learned from a deep learning neural network and applies them to the timeSVD++ CF model. Extensive experiments are run on a large Netflix rating dataset for movies. Experiment results show that the proposed hybrid recommendation model provides a good prediction for cold start items, and performs better than four existing recommendation models for rating of non-cold start items.