Exploring heterogeneous social information networks for recommendation

University of Technology Sydney. Faculty of Engineering and Information Technology.A basic premise behind our study of heterogeneous social information networks for recommendation is that a complex network structure leads to a large volume of implicit but valuable information which can significantly enhance recommendation performance. In our work, we combine the global popularity and personalized features of travel destinations and also integrate temporal sensitive patterns to form spatial-temporal wise trajectory recommendation. We then develop a model to identify representative areas of interest (AOIs) for travellers based on a large scale dataset consisting of geo-tagged images and check-ins. In addition, we introduce active time frame analysis to determine the most suitable time to visit an AOI during the day. The outcome of this work can suggest relevant personalized travel recommendations to assist people who are arriving in new cities. Another important part of our research is to study how “local” and “global” social influences exert their impact on user preferences or purchasing decisions. We first simulate the social influence diffusion in the network to find the global and local influence nodes. We then embed these two different kinds of influence data, as regularization terms, into a traditional recommendation model to improve its accuracy. We find that “Community Stars” and “Web Celebrities”, represent “local” and “global” influence nodes respectively, a phenomenon which does exist and can help us to generate significantly better recommendation results. A central topic of our thesis is also to utilize a large heterogeneous social information network to identify the collective market hyping behaviours. Combating malicious user attacks is also a key task in the recommendation research field. In our study, we investigate the evolving spam strategies which can escape from most of the traditional detection methods. Based on the investigation of the advanced spam technique, we define three kinds of heterogeneous information networks to model the patterns in such spam activities and we then propose an unsupervised learning model which combines the three networks in an attempt to discover collective hyping activities. Overall, we utilize the heterogeneous social information network to enhance recommendation quality, not only by improving the user experience and recommendation accuracy, but also by ensuring that quality and genuine information is not overwhelmed by advanced hyping activities

Topological Influence-Aware Recommendation on Social Networks

Users in online networks exert different influence during the process of information propagation, and the heterogeneous influence may contribute to personalized recommendations. In this paper, we analyse the topology of social networks to investigate users’ influence strength on their neighbours. We also exploit the user-item rating matrix to find the importance of users’ ratings and determine their influence on entire social networks. Based on the local influence between users and global influence over the whole network, we propose a recommendation method with indirect interactions that makes adequate use of users’ relationships on social networks and users’ rating data. The two kinds of influence are incorporated into a matrix factorization framework. We also consider indirect interactions between users who do not have direct links with each other. Experimental results on two real-world datasets demonstrate that our proposed framework performs better than other state-of-the-art methods for all users and cold-start users. Compared with node degrees, betweenness, and clustering coefficients, coreness constitutes the best topological descriptor to identify users’ local influence, and recommendations with the measure of coreness outperform other descriptors of user influence.</jats:p

Information Filtering on Coupled Social Networks

In this paper, based on the coupled social networks (CSN), we propose a hybrid algorithm to nonlinearly integrate both social and behavior information of online users. Filtering algorithm based on the coupled social networks, which considers the effects of both social influence and personalized preference. Experimental results on two real datasets, \emph{Epinions} and \emph{Friendfeed}, show that hybrid pattern can not only provide more accurate recommendations, but also can enlarge the recommendation coverage while adopting global metric. Further empirical analyses demonstrate that the mutual reinforcement and rich-club phenomenon can also be found in coupled social networks where the identical individuals occupy the core position of the online system. This work may shed some light on the in-depth understanding structure and function of coupled social networks

A Survey of Location Prediction on Twitter

Locations, e.g., countries, states, cities, and point-of-interests, are central to news, emergency events, and people's daily lives. Automatic identification of locations associated with or mentioned in documents has been explored for decades. As one of the most popular online social network platforms, Twitter has attracted a large number of users who send millions of tweets on daily basis. Due to the world-wide coverage of its users and real-time freshness of tweets, location prediction on Twitter has gained significant attention in recent years. Research efforts are spent on dealing with new challenges and opportunities brought by the noisy, short, and context-rich nature of tweets. In this survey, we aim at offering an overall picture of location prediction on Twitter. Specifically, we concentrate on the prediction of user home locations, tweet locations, and mentioned locations. We first define the three tasks and review the evaluation metrics. By summarizing Twitter network, tweet content, and tweet context as potential inputs, we then structurally highlight how the problems depend on these inputs. Each dependency is illustrated by a comprehensive review of the corresponding strategies adopted in state-of-the-art approaches. In addition, we also briefly review two related problems, i.e., semantic location prediction and point-of-interest recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur

The strength of lobbying and advocacy

This document is part of a digital collection provided by the Martin P. Catherwood Library, ILR School, Cornell University, pertaining to the effects of globalization on the workplace worldwide. Special emphasis is placed on labor rights, working conditions, labor market changes, and union organizing.CCC_2014_Report_Netherlands_strength_of_lobbying.pdf: 255 downloads, before Oct. 1, 2020

Synthesis report with pro-poor trade research findings and policy recommandations

The purpose of the project was to investigate international trade in fisheries products and its relationship to poverty alleviation and livelihoods of poor aquatic resource users in developing countries in Asia, and to identify options to improve the effectiveness of poverty reduction through international seafood trade. The project directly addressed the EC-PREP priority area of trade and development, and indirectly provided valuable insight to two other priority areas: food security and sustainable rural development; and institutional capacity building. [PDF contains 60 pages.