Leveraging Personalization to Facilitate Privacy
نویسندگان
چکیده
منابع مشابه
Leveraging Personalization To Facilitate Privacy
Online social networks have enabled new methods and modalities of collaboration and sharing. These advances bring privacy concerns: online social data is more accessible and persistent and simultaneously less contextualized than traditional social interactions. To allay these concerns, many web services allow users to configure their privacy settings based on a set of multiple-choice questions....
متن کاملLeveraging Shared Context to Facilitate Opportunistic Communication
Current online communication systems have neglected an important property of natural conversations; that is, many conversations are opportunistic and arise from awareness of a shared context. As a result, users of current online chat systems, for example, have to explicitly log in to predefined chat rooms with a strong goal in mind. Chatting is construed as a separate activity, and as such is s...
متن کاملLeveraging Clustering Techniques to Facilitate Metagenomic Analysis
Machine learning clustering algorithms provide excellent methods for conducting metagenomic analysis with efficiency. This study uses two machine learning algorithms, the selforganizing map and the K-means algorithms, to cluster data from an environmental sample collected from a hot springs habitat and to provide a visual analysis of that data. A data processing pipeline is described that uses ...
متن کاملPrivacy-Enhanced Web Personalization
Consumer studies demonstrate that online users value personalized content. At the same time, providing personalization on websites seems quite profitable for web vendors. This win-win situation is however marred by privacy concerns since personalizing people's interaction entails gathering considerable amounts of data about them. As numerous recent surveys have consistently demonstrated, comput...
متن کاملLeveraging Multiple Networks for Author Personalization
Recommender systems provide personalized item suggestions by identifying patterns in past user-item preferences. Most existing approaches for recommender systems work on a single domain, i.e., use user preferences from one domain and recommend items from the same domain. Recently, some recommendation models have been proposed to use user preferences from multiple related item source domains to ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SSRN Electronic Journal
سال: 2014
ISSN: 1556-5068
DOI: 10.2139/ssrn.2448026