Unbiased News Recommender Systems
Users are likely to look for news items that are consistent with their own cognitive and political views and ignore news items that are contrary to their own views. The current mainstream personalized recommendation algorithms have exacerbated this so-called ‘filter bubble effect’ phenomenon. The research showed that filter bubbles in the news domain can create serious effects, such as diminishing public discourse, and the fostering of highly polarised views amongst users, etc. This proposed project aims to build a state-of-the-art deep content-based news recommender system that can mitigate these adverse. Based on this goal, we identify a number of potential research tasks to discover accurate information behind news items and improve the performance of current news recommender systems. For example, we will explore how to generate unbiased news summarization as using unbiased news summarization as training data to train recommender systems may reduce the tendency of recommender systems to provide biased guidance; we will study multi-source news summarization to present users with comprehensive summarization of events to increase user awareness of the filter bubble effect; We will build an end-to-end news recommendation model by taking the advantages of cutting-edge deep learning and NLP technologies.