Future Directions in Recommender System: Opportunities for Improving Recommender Systems using Machine Learning and Deep Learning Techniques

Recommender systems (RS) are algorithms which provides the users with customized suggestions for the things that are most relevant to them. The vast expansion of internet content accessibility has left users with an excessive array of options. It is therefore crucial for web platforms to offer recommendations of items to each user, in order to increase user satisfaction and engagement. This paper offers a thorough overview of the main difficulties and issues encountered in the development of recommender systems, as well as a synopsis of recent research findings and solutions. Paper began with a basic overview of recommender systems, briefly outlining the many types of RSs and covered the various research projects completed to date by numerous researchers, along with their conclusions and summaries, in the related work chapter. In the next section, we conducted an exploratory analysis of the many issues and potential fixes pertaining to recommender systems. Finally, we have summed up the various metrics needed to assess recommender system quality.