Personalized news recommendation using graph-based approach

Over the past decade, there has been a proliferation of online news articles. News articles can contain rich content and contextual information pertaining to groups in societies, such as senior citizens, child rights groups, religious minorities, or environmentalist groups. In addition, news articles contain different object types such as people, organizations, statistical (numerical) information, countries, authors, or events. Thus, it is possible to create a complex heterogeneous graph containing multi-type objects (vertices) and multi-type linkages (edges) among the objects, such as common keywords found between two news articles. We call such a graph a Heterogeneous News Graph (HNG). Currently, it is possible to extract rich information and knowledge from an HNG. It is our belief that one could use an HNG to resolve the bias and visibility issues found in many news sources, as well as capture important news articles. First, due to the amount of news feeds currently available in this digital age, readers want a filtered view of relevant news articles, allowing them to focus on important (breaking) news that contain rich contextual information for their particular societal group. For example, senior citizen groups might want to know new safety measures taken by police for elderly people. Second, visibility is another problem in the world of journalism, where there are multiple objects in the news articles, such as authors and organizations. In this example, readers might need to know who are the relevant authors, or experts, for particular topics, such as Libya, Afghanistan, or climate change. To address the issues of determining importance and visibility of objects, we propose novel graph-based, approaches using HNGs that will (1) rank the expertness of an article's author on a specific topic, and (2) identify articles of particular interest and value. In summary, we propose a novel graph-based approach for determining context and content in news articles so that more personalized recommendations can be realized.