Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
The Journal of Machine Learning Research
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Tagommenders: connecting users to items through tags
Proceedings of the 18th international conference on World wide web
Transfer learning for collaborative filtering via a rating-matrix generative model
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Can movies and books collaborate?: cross-domain collaborative filtering for sparsity reduction
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A generic semantic-based framework for cross-domain recommendation
Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems
Transfer learning to predict missing ratings via heterogeneous user feedbacks
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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Cross domain recommendation and preferences association are emerging research topics. In this paper, we will study the two topics through experimental analysis methods: firstly, we use folksonamy to analyze the preferences association among different domains; secondly, we analyze the feasibility of cross domain rating prediction based on KNN model. The experimental results report the associative tag pairs of users' preferences on items across domains. In addition, we report the cross domain prediction results here.