Fuzzy queries in multimedia database systems
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
CubeSVD: a novel approach to personalized Web search
WWW '05 Proceedings of the 14th international conference on World Wide Web
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
Cross-language information retrieval using PARAFAC2
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Structural and temporal analysis of the blogosphere through community factorization
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A probabilistic framework for relational clustering
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
GraphScope: parameter-free mining of large time-evolving graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Facetnet: a framework for analyzing communities and their evolutions in dynamic networks
Proceedings of the 17th international conference on World Wide Web
Algorithms for sparse nonnegative tucker decompositions
Neural Computation
Probabilistic polyadic factorization and its application to personalized recommendation
Proceedings of the 17th ACM conference on Information and knowledge management
iOLAP: A framework for analyzing the internet, social networks, and other networked data
IEEE Transactions on Multimedia - Special section on communities and media computing
Tensor Decompositions and Applications
SIAM Review
Fast metadata-driven multiresolution tensor decomposition
Proceedings of the 20th ACM international conference on Information and knowledge management
Approximate tensor decomposition within a tensor-relational algebraic framework
Proceedings of the 20th ACM international conference on Information and knowledge management
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Non-negative tensor factorization (NTF) is a relatively new technique that has been successfully used to extract significant characteristics from polyadic data, such as data in social networks. Because these polyadic data have multiple dimensions (e.g., the author, content, and timestamp of a blog post), NTF fits in naturally and extracts data characteristics jointly from different data dimensions. In the standard NTF, all information comes from the observed data and end users have no control over the outcomes. However, in many applications very often the end users have certain prior knowledge, such as the demographic information about individuals in a social network or a pre-constructed ontology on the contents, and therefore prefer the extracted data characteristics being consistent with such prior knowledge. To allow users' prior knowledge to be naturally incorporated into NTF, in this paper we present a novel framework - FacetCube - that extends the standard non-negative tensor factorization. The new framework allows the end users to control the factorization outputs at three different levels for each of the data dimensions. The proposed framework is intuitively appealing in that it has a close connection to the probabilistic generative models. In addition to introducing the framework, we provide an iterative algorithm for computing the optimal solution to the framework. We also develop an efficient implementation of the algorithm that consists of a series of techniques to make our framework scalable to large data sets. Extensive experimental studies on a paper citation data set and a blog data set demonstrate that our new framework is able to effectively incorporate users' prior knowledge, improves performance over the standard NTF on the task of personalized recommendation, and is scalable to large data sets from real-life applications.