An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Decentralized mediation of user models for a better personalization
AH'06 Proceedings of the 4th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Distributed collaborative filtering with domain specialization
Proceedings of the 2007 ACM conference on Recommender systems
Locally Adaptive Neighborhood Selection for Collaborative Filtering Recommendations
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Accuracy in Rating and Recommending Item Features
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Domain ranking for cross domain collaborative filtering
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
GeniUS: generic user modeling library for the social semantic web
JIST'11 Proceedings of the 2011 joint international conference on The Semantic Web
TALMUD: transfer learning for multiple domains
Proceedings of the 21st ACM international conference on Information and knowledge management
Personalized recommendation via cross-domain triadic factorization
Proceedings of the 22nd international conference on World Wide Web
Cross-domain collaborative filtering via bilinear multilevel analysis
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Mining novelty-seeking trait across heterogeneous domains
Proceedings of the 23rd international conference on World wide web
Experimental evaluation of context-dependent collaborative filtering using item splitting
User Modeling and User-Adapted Interaction
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One of the main problems of collaborative filtering recommenders is the sparsity of the ratings in the users-items matrix, and its negative effect on the prediction accuracy. This paper addresses this issue applying cross-domain mediation of collaborative user models, i.e., importing and aggregating vectors of users' ratings stored by collaborative systems operating in different application domains. The paper presents several mediation approaches and initial experimental evaluation demonstrating that the mediation can improve the accuracy of the generated predictions.