{"title":"TDRec: Enhancing Social Recommendation Using Both Trust and Distrust Information","authors":"Tiansheng Bai, Bo Yang, Fei Li","doi":"10.1109/ENIC.2015.17","DOIUrl":"https://doi.org/10.1109/ENIC.2015.17","url":null,"abstract":"Traditional Collaborative Filtering has been one of the most widely used recommender systems, unfortunately it suffers from cold-start and data sparsity problems. With the development of social networks, more recommendation systems are trying to generate more eligible recommendation through excavating users' potential preferences using their social relationships. Almost all social recommender systems employ only positive inter-user relations such as friendship or trust information. However, incorporating negative relations in recommendation has not been investigated thoroughly in literature. In this paper, we propose a novel model-based method which takes advantage of both positive and negative inter-user relations. We apply matrix factorization techniques and utilize both rating and trust information to learn users' reasonable latent preference. We also incorporate two regularization terms to take distrust information into consideration. Our experiments on real-world and open datasets demonstrate the superiority of our model over the other state-of-the-art methods.","PeriodicalId":231985,"journal":{"name":"2015 Second European Network Intelligence Conference","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129102449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Israel Mendonça, R. Figueiredo, Vincent Labatut, P. Michelon
{"title":"Relevance of Negative Links in Graph Partitioning: A Case Study Using Votes from the European Parliament","authors":"Israel Mendonça, R. Figueiredo, Vincent Labatut, P. Michelon","doi":"10.1109/ENIC.2015.25","DOIUrl":"https://doi.org/10.1109/ENIC.2015.25","url":null,"abstract":"In this paper, we want to study the informative value of negative links in signed complex networks. For this purpose, we extract and analyze a collection of signed networks representing voting sessions of the European Parliament (EP). We first process some data collected by the Vote Watch Europe Website for the whole 7th term (2009-2014), by considering voting similarities between Members of the EP to define weighted signed links. We then apply a selection of community detection algorithms, designed to process only positive links, to these data. We also apply Parallel Iterative Local Search (Parallel ILS), an algorithm recently proposed to identify balanced partitions in signed networks. Our results show that, contrary to the conclusions of a previous study focusing on other data, the partitions detected by ignoring or considering the negative links are indeed remarkably different for these networks. The relevance of negative links for graph partitioning therefore is an open question which should be further explored.","PeriodicalId":231985,"journal":{"name":"2015 Second European Network Intelligence Conference","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129670954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Detecting Real-World Influence through Twitter","authors":"Jean-Valère Cossu, Nicolas Dugué, Vincent Labatut","doi":"10.1109/ENIC.2015.20","DOIUrl":"https://doi.org/10.1109/ENIC.2015.20","url":null,"abstract":"In this paper, we investigate the issue of detecting the real-life influence of people based on their Twitter account. We propose an overview of common Twitter features used to characterize such accounts and their activity, and show that these are inefficient in this context. In particular, retweets and followers numbers, and Klout score are not relevant to our analysis. We thus propose several Machine Learning approaches based on Natural Language Processing and Social Network Analysis to label Twitter users as Influencers or not. We also rank them according to a predicted influence level. Our proposals are evaluated over the CLEF RepLab 2014 dataset, and outmatch state-of-the-art ranking methods.","PeriodicalId":231985,"journal":{"name":"2015 Second European Network Intelligence Conference","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115546596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Exploring Patterns in European Singles Charts","authors":"A. Buda, A. Jarynowski","doi":"10.1109/ENIC.2015.27","DOIUrl":"https://doi.org/10.1109/ENIC.2015.27","url":null,"abstract":"European singles charts are important part of the music industry responsible for creating popularity of songs. After modeling and exploring dynamics of global album sales in previous papers, we investigate patterns of hit singles popularity according to all data (1966-2015) from weekly charts (polls) in 12 Western European countries. The dynamics of building popularity in various national charts is more than the economy because it depends on spread of information. In our research we have shown how countries may be affected by their neighbourhood and influenced by technological era. We have also computed correlations with geographical and cultural distances between countries in analog, digital and Internet era. We have shown that time delay between the single premiere and the peak of popularity has become shorter under the influence of technology and the popularity of songs depends on geographical distances in analog (1966-1987) and Internet (2004-2015) era. On the other hand, cultural distances between nations have influenced the peaks of popularity, but in the Compact Disc era only (1988-2003). We have also indicated the European countries \"in line\" with global trends e.g. The Netherlands, the United Kingdom and outsiders like Italy and Spain.","PeriodicalId":231985,"journal":{"name":"2015 Second European Network Intelligence Conference","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128899074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}