Abstract:
Over the last few years, Recommender Systems (RS) have shown massive growth
and become increasingly essential as web service giants like “youtube” and
“Netflix” skyrocketed in terms of popularity., RS can be defined as algorithms
that attempt to suggest relevant products to consumers. Collaborative filtering,
content-based, and hybrid recommendation methods are the primary
recommendation methods that will be discussed in this work. This paper also
covers the basics and potential ways to increase the relevance and competence of
RS and the limitations and constraints of the current recommendation approach,
including the cold-start problem, stability vs plasticity problem, sparsity issues,
etc. This study provides a comprehensive overview of current state-of-the-art
Recommender System methods are utilized in several application areas. A systematic
review was conducted using highly referenced literature discovered on Google
Scholars then filtered down to the most current and relevant studies in the RS
field. This study aims to provide researchers and industrial developers with a
concise guide to recommender systems through a systematic analysis.
