A Critical Survey On Privacy Prevelling In Collaborative Filtring Recomender System: Challenges, State-Of-The-Art Methods And Future Directions

Recommender systems have become very popular in recent years and are used in different fields of technology. Memory-based Collaborative Filtering (CF) Recommender System is a quickly progressing study area and proved to be doing well for different types of recommender systems. Memory-based CF recommends items based on the entire collection of items which have been rated by users previously. CF generates recommendations on the basis of neighbors in memory and known as Memory based CF. In this study, we have discussed a major challenge for recommender system that has direct access to user data that leads to privacy risk and may become the cause of attacks and other risks. Leading to the above challenges, we have conducted a comprehensive survey of different risks and analyzed them critically. Moreover, we have investigated the existing state-of-the-art approaches which are being used to address such challenges. Based on findings, the current study will come up with existing approaches that reduce privacy risks for Memory-based CF.