Recommender systems for contextually-aware, versioned items

Abstract

While existing Recommender systems assume items are fixed entities, this research considers situations where there can be different versions of an item. We propose a process that is a type of contextually-aware post filtering for recommending items, and illustrate the system with real data from a newspaper. The novel framework decides whether or not to recommend particular news articles based on news trend and incorporates user states as additional contextual information and recommends versioned items based on user preferences.