Collaborative filtering with privacy via factor analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Collaborative Filtering with Privacy
SP '02 Proceedings of the 2002 IEEE Symposium on Security and Privacy
Collaborative filtering via gaussian probabilistic latent semantic analysis
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Ontologically-Enriched unified user modeling for cross-system personalization
UM'05 Proceedings of the 10th international conference on User Modeling
User model interoperability: a survey
User Modeling and User-Adapted Interaction
TUMS: twitter-based user modeling service
ESWC'11 Proceedings of the 8th international conference on The Semantic Web
Personalization and privacy: a survey of privacy risks and remedies in personalization-based systems
User Modeling and User-Adapted Interaction
Proceedings of the 2nd International Conference on Learning Analytics and Knowledge
High order pLSA for indexing tagged images
Signal Processing
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Recommender systems have been steadily gaining popularity and have been deployed by several service providers. Large scalable deployment has however highlighted one of the design problems of recommender systems: lack of interoperability. Users today often use multiple electronic systems offering recommendations, which cannot learn from one another. The result is that the end user has to often provide similar information and in some cases disjoint information. Intuitively, it seems that much can be improved with this situation: information learnt by one system could potentially be reused by another, to offer an overall improved personalization experience. In this paper, we provide an effective solution to this problem using Latent Semantic Models by learning a user model across multiple systems. A privacy preserving distributed framework is added around the traditional Probabilistic Latent Semantic Analysis framework, and practical aspects such as addition of new systems and items are also dealt with in this work.