Communications of the ACM
IPTPS '01 Revised Papers from the First International Workshop on Peer-to-Peer Systems
Proceedings of the 10th international conference on Intelligent user interfaces
Privacy-Preserving Top-N Recommendation on Horizontally Partitioned Data
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Supporting Context-Aware Media Recommendations for Smart Phones
IEEE Pervasive Computing
Robust cooperative trust establishment for MANETs
Proceedings of the fourth ACM workshop on Security of ad hoc and sensor networks
LARS: a locally aware reputation system for mobile ad hoc networks
Proceedings of the 44th annual Southeast regional conference
The truth about lying in online dating profiles
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Discovering and Exploiting Causal Dependencies for Robust Mobile Context-Aware Recommenders
IEEE Transactions on Knowledge and Data Engineering
Privacy-Preserving Recommendation Systems for Consumer Healthcare Services
ARES '08 Proceedings of the 2008 Third International Conference on Availability, Reliability and Security
Personalized Context-Aware Recommendations in SMARTMUSEUM: Combining Semantics with Statistics
SEMAPRO '09 Proceedings of the 2009 Third International Conference on Advances in Semantic Processing
A Privacy-Preserving Book Recommendation Model Based on Multi-agent
IWCSE '09 Proceedings of the 2009 Second International Workshop on Computer Science and Engineering - Volume 02
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
A NMF-Based Privacy-Preserving Recommendation Algorithm
ICISE '09 Proceedings of the 2009 First IEEE International Conference on Information Science and Engineering
Privacy-Preserving Collaborative Filtering Protocol Based on Similarity between Items
AINA '10 Proceedings of the 2010 24th IEEE International Conference on Advanced Information Networking and Applications
An Approach for Context-Aware Service Discovery and Recommendation
ICWS '10 Proceedings of the 2010 IEEE International Conference on Web Services
Proceedings of the Workshop on Context-Aware Movie Recommendation
Workshop on Context-aware Movie Recommendation 2010
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Improving Privacy-Preserving NBC-Based Recommendations by Preprocessing
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
AdContRep: a privacy enhanced reputation system for MANET content services
UIC'10 Proceedings of the 7th international conference on Ubiquitous intelligence and computing
Message Receiver Determination in Multiple Simultaneous IM Conversations
IEEE Intelligent Systems
Information theoretic framework of trust modeling and evaluation for ad hoc networks
IEEE Journal on Selected Areas in Communications
On trust models and trust evaluation metrics for ad hoc networks
IEEE Journal on Selected Areas in Communications
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Mobile ad hoc network (MANET) has become a practical platform for pervasive services. Various user data could be requested for accessing such a service. However, it is normally difficult for a user to justify whether it is safe and proper to disclose personal data to others in different contexts. For solving this problem, we propose AdPriRec, a context-aware recommender system for preserving user privacy in MANET services. To support frequent changes of node pseudonyms in MANET, we develop a hybrid recommendation generation solution. We apply a trusted recommendation sever who knows the node's real identity to calculate a recommendation vector based on long term historical experiences. The vector can be also generated at each MANET node according to recent experiences accumulated based on node pseudonyms, while this vector could be further fine-tuned when the recommendation server is accessible. We design a number of algorithms for AdPriRec to generate context-aware recommendations for MANET users. The recommendation vector is calculated based on a number of factors such as data sharing behaviors and behavior correlation, service popularity and context, personal data type, community information of nodes and trust value of each involved party. An example based evaluation illustrates the usage and implication of the factors and shows AdPriRec's effectiveness. A prototype implementation based on Nokia N900 further proves the concept of AdPriRec design.