Personalized news recommendation based on implicit feedback
Ilija Ilievski, Sujoy Roy · 2013
This paper presents a personalized news recommendation system that combines effective ways of understanding new articles with novel ways of modelling evolving user interest profiles to deliver relevant news articles to a user. A news article is represented as a taxonomy of hierarchical abstractions that capture different semantic facets of the news story. A users interest profile is modelled as an evolving interest over these facets. Users interest in individual articles is determined using a novel SWL (select-watch-leave) interest modelling framework that leverages on a detailed analysis of his usage history. Initial performance comparisons with state-of-the art personalized ranking approaches[2] are promising.