Robust probabilistic tensor analysis for time-variant collaborative filtering

Authors:
Jing Pan;Zhao Ma;Yanwei Pang;Yuan Yuan
Affiliations:
School of Electronic Engineering, Tianjin University of Technology and Education, Tianjin 300222, China;School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China;School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China;Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, ...
Venue:
Neurocomputing
Year:
2013

Citing 21
Cited 0

Eigentaste: A Constant Time Collaborative Filtering Algorithm

Information Retrieval
Latent dirichlet allocation

The Journal of Machine Learning Research
Evaluating collaborative filtering recommender systems

ACM Transactions on Information Systems (TOIS)
Latent semantic models for collaborative filtering

ACM Transactions on Information Systems (TOIS)
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo

Proceedings of the 25th international conference on Machine learning
Collaborative filtering with temporal dynamics

Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-HDP: a non parametric Bayesian model for tensor factorization

AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Binary sparse nonnegative matrix factorization

IEEE Transactions on Circuits and Systems for Video Technology
Correlative linear neighborhood propagation for video annotation

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Metric learning with feature decomposition for image categorization

Neurocomputing
Non-goal scene analysis for soccer video

Neurocomputing
Summarizing tourist destinations by mining user-generated travelogues and photos

Computer Vision and Image Understanding
Max-Min Distance Analysis by Using Sequential SDP Relaxation for Dimension Reduction

IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Sparse Tensor Decomposition by Probabilistic Latent Semantic Analysis

ICIG '11 Proceedings of the 2011 Sixth International Conference on Image and Graphics
Probabilistic latent semantic analysis

UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Image Annotation by Graph-Based Inference With Integrated Multiple/Single Instance Representations

IEEE Transactions on Multimedia
Two dimensional principal components of natural images and its application

Neurocomputing
Bayesian Tensor Approach for 3-D Face Modeling

IEEE Transactions on Circuits and Systems for Video Technology
Robust Tensor Analysis With L1-Norm

IEEE Transactions on Circuits and Systems for Video Technology
Non-Negative Patch Alignment Framework

IEEE Transactions on Neural Networks
Manifold Regularized Discriminative Nonnegative Matrix Factorization With Fast Gradient Descent

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.01

Visualization

Abstract

The input data of collaborative filtering, also known as recommendation system, are usually sparse and noisy. In addition, in many cases the data are time-variant and have obvious periodic property. In this paper, we take the two characteristics into account. To utilize the time-variant and periodic properties, we describe the data as a three-order tensor and then formulate the collaborative filtering as a problem of probabilistic tensor decomposition with a time-periodical constraint. The robustness is achieved by employing Tsallis divergence to describe the objective function and q-EM algorithm to find the optimal solution. The proposed method is demonstrated on movie recommendation. Experimental results on two Netflix and Movielens databases show the superiority of the proposed method.