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Some recent work proposed differential private collaborative filtering (DPCF) recommender systems to protect user privacy from indirect access attacks, e.g., KNN attacks. As the cost of such protection, the MAE of recommendation was retained with insignificant increase but the more focused metrics, i.e., the precision and recall of top-k recommendations, degraded unacceptable. To address this problem,...
A recommendation system learns a user's interests from her historical purchasing or watching behavior, which is disclosed to the recommendation system inevitably. Such a disclosure raises a serious concern in the public for the leak of users' privacy. For instance, a person who watches a lot of videos which are more preferred by women than men can be inferred as female. Recently, as a response to...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.