Social Transparency through Recommendation Engines and its Challenges: Looking Beyond Privacy

Our knowledge society is quickly becoming a ‘transparent’ one. This transparency is acquired, among other means, by ’personalization’ or ‘profiling’: ICT tools gathering contextualized information about individuals in men–computers interactions. The paper begins with an overview of these ICT tools (behavioral targeting, recommendation engines, ‘personalization’ through social networking). Based on these developments the analysis focus a case study of developments in social network (Facebook) and the trade-offs between ‘personalization’ and privacy constrains. A deeper analysis will reveal unexpected challenges and the need to overcome the privacy paradigm. Finally a draft of possible normative solutions will be depicted, grounded in new forms of individual rights.Recommendation Engines, Profiling, Privacy, ‘Sui Generis’ Copyright

A Better Understanding of College Students\u27 YouTube Behaviors

The purpose of this research study is to get a closer look into the behavior of college students towards the video streaming website YouTube. The objective is to understand whether the benefits of publishing videos on the site are positive for business organizations. The study looks at many variables that would help companies better understand what exactly publishing a video on YouTube would do for them. These variables include gender, hours of television watched, hours of Internet used, hours spent reading and whether a video is made by a regular user or a professional company. It was found that males are more likely to use YouTube then females, despite using the Internet much less. It was also shown that there are both pros and cons for implementing user and corporate developed videos

Personalized News Recommender using Twitter

Online news reading has become a widely popular way to read news articles from news sources around the globe. With the enormous amount of news articles available, users are easily swamped by information of little interest to them. News recommender systems are one approach to help users find interesting articles to read. News recommender systems present the articles to individual users based on their interests rather than presenting articles in order of their occurrence. In this thesis, we present our research on developing personalized news recommendation system with the help of a popular micro-blogging service Twitter . The news articles are ranked based on the popularity of the article that is identified with the help of the tweets from the Twitter\u27s public timeline. Also, user profiles are built based on the user\u27s interests and the news articles are ranked by matching the characteristics of the user profile. With the help of these two approaches, we present a hybrid news recommendation model that recommends interesting news stories to the user based on their popularity and their relevance to the user profile

Social software for music

Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 200