GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Proceedings of the 1998 conference on Advances in neural information processing systems II
A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
Relation between PLSA and NMF and implications
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Sparse Image Coding Using a 3D Non-Negative Tensor Factorization
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Non-negative tensor factorization with applications to statistics and computer vision
ICML '05 Proceedings of the 22nd international conference on Machine learning
Combining content and link for classification using matrix factorization
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Topic modeling with network regularization
Proceedings of the 17th international conference on World Wide Web
Learning multiple graphs for document recommendations
Proceedings of the 17th international conference on World Wide Web
MetaFac: community discovery via relational hypergraph factorization
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
iOLAP: A framework for analyzing the internet, social networks, and other networked data
IEEE Transactions on Multimedia - Special section on communities and media computing
Multi-modal multi-correlation person-centric news retrieval
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
FacetCube: a framework of incorporating prior knowledge into non-negative tensor factorization
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Community Discovery via Metagraph Factorization
ACM Transactions on Knowledge Discovery from Data (TKDD)
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Multiple-dimensional, i.e., polyadic, data exist in many applications, such as personalized recommendation and multiple-dimensional data summarization. Analyzing all the dimensions of polyadic data in a principled way is a challenging research problem. Most existing methods separately analyze the marginal relationships among pairwise dimensions and then combine the results afterwards. Motivated by the fact that various dimensions of polyadic data jointly affect each other, we propose a probabilistic polyadic factorization approach to directly model all the dimensions simultaneously in a unified framework. We then show the connection between the probabilistic polyadic factorization and a non-negative version of the Tucker tensor factorization. We provide detailed theoretical analysis of the new modeling framework, discuss implementation techniques for our models, and propose several extensions to the basic framework. We then apply the proposed models to the application of personalized recommendation. Extensive experiments on a social bookmarking dataset, Delicious, and a paper citation dataset, CiteSeer, demonstrate the effectiveness of the proposed models.