Study on privacy preserving recommender systems datasets
Rahul Katarya, Anil Singh Parihar · 2017 International Conference on Inventive Computing and Informatics (ICICI) · 2017
Recommender system are information filtering software tools, which are responsible to deliver information to end user in a safe environment. However, as soon as we are moving towards big data then privacy in particular data exchange becomes unsafe. In this paper, we will discuss the information used by the recommender systems during privacy preserving process. Various types of data have been adopted by social recommender systems we will highlight the issue and paradigm on such data related to privacy concern. As the privacy of particular user become public when interact in social recommender systems, we discussed the various data applications in which privacy issue is discussed by various authors.