Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Learning from a mixture of labeled and unlabeled examples with parametric side information
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
IEEE Transactions on Knowledge and Data Engineering
Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents
Engineering Applications of Artificial Intelligence
Introduction to Semi-Supervised Learning
Introduction to Semi-Supervised Learning
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Privacy wizards for social networking sites
Proceedings of the 19th international conference on World wide web
Semi-supervised feature selection for graph classification
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Semi-Supervised Learning
User Centric Policy Management in Online Social Networks
POLICY '10 Proceedings of the 2010 IEEE International Symposium on Policies for Distributed Systems and Networks
Visualizing privacy implications of access control policies in social network systems
DPM'09/SETOP'09 Proceedings of the 4th international workshop, and Second international conference on Data Privacy Management and Autonomous Spontaneous Security
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Fine grain policy settings in social network sites is becoming a very important requirement for managing user's privacy. Incorrect privacy policy settings can easily lead to leaks in private and personal information. At the same time, being too restrictive would reduce the benefits of online social networks. This is further complicated with the growing adoption of social networks and with the rapid growth in information uploading and sharing. The problem of facilitating policy settings has attracted numerous access control, and human computer interaction researchers. The solutions proposed range from usable interfaces for policy settings to automated policy settings. We propose a fine grained policy recommendation system that is based on an iterative semi-supervised learning approach that uses the social graph propagation properties. Active learning and social graph properties were used to detect the most informative instances to be labeled as training sets. We implemented and tested our approach using real Facebook dataset. We compared our proposed approach to supervised learning and random walk approaches. Our proposed approaches provided high accuracy and precision when compared to the other approaches.