Evolution of the user's content: An Overview of the state of the art
It provides an overview for researchers and practitioners in recommender systems, but is incremental as it summarizes existing work without novel contributions.
This paper addresses the problem of accurately recommending content as user interests evolve, by reviewing existing research on recommender systems that adapt to changing user preferences, without presenting new results or numbers.
The evolution of the user's content still remains a problem for an accurate recommendation.This is why the current research aims to design Recommender Systems (RS) able to continually adapt information that matches the user's interests. This paper aims to explain this problematic point in outlining the proposals that have been made in research with their advantages and disadvantages.