Paradigms for Incorporating Context in CARS
Monika Anand, Chhavi Rana · 2013
Recommender systems are a subclass of information filtering systems that predict the 'rating' or 'preference' that a user would give to an item. Most traditional Recommender Systems (RSs) focus on recommending the most relevant items to individual users and do not take into consideration the circumstances and other contextual information such as time, place and company of other people when recommendations take place. This paper presents the general notion of context and how it can be modeled in recommender systems. Furthermore, we introduce three different algorithmic paradigms- Contextual Pre- filtering, Post-filtering, and Contextual modeling for incorporating contextual information into the recommendation process. This paper reviews the possibilities of combining several context-aware recommendation techniques into a single unifying approach.