Reader centric real-time electric magazine article generator

摘要

A real-time E-magazine article generation system that uses two article recommendation systems have been developed. The first recommendation system is called the Relevance-Based Recommender, which uses mutual information, and the second is called the Reading History Based Recommender, which uses a confabulation model. Both systems were found to recommend suitable articles.