Evolution of the user's content: An Overview of the state of the art

TL;DR

This paper reviews current research on adaptive recommender systems that evolve with user content to improve recommendation accuracy, highlighting existing approaches and their strengths and weaknesses.

Contribution

It provides a comprehensive overview of state-of-the-art methods for evolving recommender systems and discusses their respective advantages and disadvantages.

Findings

Identifies key challenges in evolving recommender systems.

Summarizes various approaches and their effectiveness.

Highlights gaps and future directions in the field.

Abstract

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.