Incomplete Multi-view Clustering via Graph Regularized Matrix Factorization

Collaborative filtering suffers from the problems of data sparsity and cold start, which dramatically degrade recommendation performance. To help resolve these issues, we propose TrustSVD, a trust-based matrix factorization technique. By analyzing the social trust data from four real-world data sets, we conclude that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. Hence, we build on top of a state-of-the-art recommendation algorithm SVD++ which inherently involves the explicit and implicit influence of rated items, by further incorporating both the explicit and implicit influence of trusted users on the prediction of items for an active user. To our knowledge, the work reported is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that our approach TrustSVD achieves better accuracy than other ten counterparts, and can better handle the concerned issues.

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A new method on class-of-interest oriented spectral unmixing based on nonnegative matrix factorization

Remote Sensing and Smart City ◽

10.2495/rssc140401 ◽

2014 ◽

Author(s):

L.G. Wang ◽

Z.J. Zhao ◽

S.Y. Hao

Keyword(s):

Matrix Factorization ◽

Nonnegative Matrix Factorization ◽

Nonnegative Matrix ◽

Spectral Unmixing ◽

New Method

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Mitigating the Effect of Out-of-Vocabulary Entity Pairs in Matrix Factorization for KB Inference

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/573 ◽

2018 ◽

Cited By ~ 1

Author(s):

Prachi Jain ◽

Shikhar Murty ◽

Mausam . ◽

Soumen Chakrabarti

Keyword(s):

Knowledge Base ◽

Hybrid Model ◽

Matrix Factorization ◽

Relation Extraction ◽

Tensor Factorization ◽

Evaluation Protocol

This paper analyzes the varied performance of Matrix Factorization (MF) on the related tasks of relation extraction and knowledge-base completion, which have been unified recently into a single framework of knowledge-base inference (KBI) [Toutanova et al., 2015]. We first propose a new evaluation protocol that makes comparisons between MF and Tensor Factorization (TF) models fair. We find that this results in a steep drop in MF performance. Our analysis attributes this to the high out-of-vocabulary (OOV) rate of entity pairs in test folds of commonly-used datasets. To alleviate this issue, we propose three extensions to MF. Our best model is a TF-augmented MF model. This hybrid model is robust and obtains strong results across various KBI datasets.

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