GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Computing and applying trust in web-based social networks
Computing and applying trust in web-based social networks
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Transfer learning for collaborative filtering via a rating-matrix generative model
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
TrustWalker: a random walk model for combining trust-based and item-based recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning optimal ranking with tensor factorization for tag recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
Distributed nonnegative matrix factorization for web-scale dyadic data analysis on mapreduce
Proceedings of the 19th international conference on World wide web
Recommender systems with social regularization
Proceedings of the fourth ACM international conference on Web search and data mining
A unified framework for recommendations based on quaternary semantic analysis
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
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
A transitivity aware matrix factorization model for recommendation in social networks
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Learning personal + social latent factor model for social recommendation
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Given the vast amount of information on the World Wide Web, recommender systems are increasingly being used to help filter irrelevant data and suggest information that would interest users. Traditional systems make recommendations based on a single domain e.g., movie or book domain. Recent work has examined the correlations in different domains and designed models that exploit user preferences on a source domain to predict user preferences on a target domain. However, these methods are based on matrix factorization and can only be applied to two-dimensional data. Transferring high dimensional data from one domain to another requires decomposing the high dimensional data to binary relations which results in information loss. Furthermore, this decomposition creates a large number of matrices that need to be transferred and combining them in the target domain is non-trivial. Separately, researchers have looked into using social network information to improve recommendation. However, this social network information has not been explored in cross domain collaborative filtering. In this work, we propose a generalized cross domain collaborative filtering framework that integrates social network information seamlessly with cross domain data. This is achieved by utilizing tensor factorization with topic based social regularization. This framework is able to transfer high dimensional data without the need for decomposition by finding shared implicit cluster-level tensor from multiple domains. Extensive experiments conducted on real world datasets indicate that the proposed framework outperforms state-of-art algorithms for item recommendation, user recommendation and tag recommendation.