712 research outputs found
Reconsidering the Role of Conflict in the Lives of Refugees: The Case of Somalis in Europe
Based upon qualitative research with Somali refugees in two European host countries â the UK and the Netherlands - this paper explores the micro-level experiences and ongoing effects of the Somali conflict on their lives in exile. Challenging predominant macro-level framings of refugees in these settings, it supports a micro-level analysis of their experiences and lives. It analyses their ongoing connections with the conflict in Somalia, and reveals how this can affect aspects of their integration and emotional health while in exile, alongside social problems such as poverty, drug use and divorce.
CnGAN: Generative Adversarial Networks for Cross-network user preference generation for non-overlapped users
A major drawback of cross-network recommender solutions is that they can only
be applied to users that are overlapped across networks. Thus, the
non-overlapped users, which form the majority of users are ignored. As a
solution, we propose CnGAN, a novel multi-task learning based,
encoder-GAN-recommender architecture. The proposed model synthetically
generates source network user preferences for non-overlapped users by learning
the mapping from target to source network preference manifolds. The resultant
user preferences are used in a Siamese network based neural recommender
architecture. Furthermore, we propose a novel user based pairwise loss function
for recommendations using implicit interactions to better guide the generation
process in the multi-task learning environment.We illustrate our solution by
generating user preferences on the Twitter source network for recommendations
on the YouTube target network. Extensive experiments show that the generated
preferences can be used to improve recommendations for non-overlapped users.
The resultant recommendations achieve superior performance compared to the
state-of-the-art cross-network recommender solutions in terms of accuracy,
novelty and diversity
Towards Comprehensive Recommender Systems: Time-Aware UnifiedcRecommendations Based on Listwise Ranking of Implicit Cross-Network Data
The abundance of information in web applications make recommendation
essential for users as well as applications. Despite the effectiveness of
existing recommender systems, we find two major limitations that reduce their
overall performance: (1) inability to provide timely recommendations for both
new and existing users by considering the dynamic nature of user preferences,
and (2) not fully optimized for the ranking task when using implicit feedback.
Therefore, we propose a novel deep learning based unified cross-network
solution to mitigate cold-start and data sparsity issues and provide timely
recommendations for new and existing users.Furthermore, we consider the ranking
problem under implicit feedback as a classification task, and propose a generic
personalized listwise optimization criterion for implicit data to effectively
rank a list of items. We illustrate our cross-network model using Twitter
auxiliary information for recommendations on YouTube target network. Extensive
comparisons against multiple time aware and cross-network base-lines show that
the proposed solution is superior in terms of accuracy, novelty and diversity.
Furthermore, experiments conducted on the popular MovieLens dataset suggest
that the proposed listwise ranking method outperforms existing state-of-the-art
ranking techniques
Mind Your Language: Abuse and Offense Detection for Code-Switched Languages
In multilingual societies like the Indian subcontinent, use of code-switched
languages is much popular and convenient for the users. In this paper, we study
offense and abuse detection in the code-switched pair of Hindi and English
(i.e. Hinglish), the pair that is the most spoken. The task is made difficult
due to non-fixed grammar, vocabulary, semantics and spellings of Hinglish
language. We apply transfer learning and make a LSTM based model for hate
speech classification. This model surpasses the performance shown by the
current best models to establish itself as the state-of-the-art in the
unexplored domain of Hinglish offensive text classification.We also release our
model and the embeddings trained for research purpose
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