Exploring Comprehensive Privacy Solutions for Enhancing Recommender System Security and Utility

Nowadays recommender systems have gained large attention and have become highly efficient tools for categorizing and personalizing the different requirements of the users in online mode. Recommender systems are driven by the evolving preferences of computer users and the increasing accessibility of the internet. Though they can provide precise recommendations, modern recommender systems face numerous constraints and challenges, such as cold-start problem, sparsity, scalability, privacy concerns, and optimization issues. There are diverse types of techniques available which in turn complicate the process of appropriate or valid selection, while building the application-focused recommender systems. Every technique possesses its own unique set of features, having advantages and disadvantages which thus creates a necessity for doing a comprehensive investigation to focus on the complexities involved. This research work aims to conduct a systematic assessment of current contributions in the field of recommender systems, with the objective of gaining a thorough understanding of the advancements, identifying areas that require further attention, and elucidating the unresolved questions and concerns associated with different techniques. By synthesizing the findings of this review, valuable insights can be obtained to guide for the future research work and advancement efforts in the realm of recommender systems.