Implementing a content-based recommender system for news readers
University of New Brunswick
Recommender systems are widely used to suggest items to users based on users' interests. Content-based recommender systems are popular, specifically in the area of news services. This report describes the implementation of an effective online news recommender system by combining two different algorithms. Our first algorithm employs users' activity histories as inputs. Then it processes this data using a Bayesian framework to predict users' genuine interests, and as a result suggests new articles based on those interests. The other algorithm attempts to find keyword matches among the user's keywords and new articles' keywords to suggest new articles to that user. The Java language was used to implement these algorithms. To test the system, ten different users were chosen randomly among those users who posted comments for more than 50 articles from 2012/05/01 to 2012/07/30. These experiments show that our system successfully suggested new articles to users based on their fields of interest.