Convolution-consistent collective matrix completion

Xu Liu, Jingrui He, Sam Duddy, Liz O'Sullivan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Collective matrix completion refers to the problem of simultaneously predicting the missing entries in multiple matrices by leveraging the cross-matrix information. It finds abundant applications in various domains such as recommender system, dimensionality reduction, and image recovery. Most of the existing work represents the cross-matrix information in a shared latent structure constrained by the Euclidean-based pairwise similarity, which may fail to capture the nonlinear relationship of the data. To address this problem, in this paper, we propose a new collective matrix completion framework, named C4, which uses the graph spectral filters to capture the non-Euclidean cross-matrix information. To the best of our knowledge, this is the first effort to represent the cross-matrix information in the graph spectral domain. We benchmark our model against 8 recent models on 10 real-world data sets, and our model outperforms state-of-the-art methods in most tasks.

Original languageEnglish (US)
Title of host publicationCIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages2209-2212
Number of pages4
ISBN (Electronic)9781450369763
DOIs
StatePublished - Nov 3 2019
Event28th ACM International Conference on Information and Knowledge Management, CIKM 2019 - Beijing, China
Duration: Nov 3 2019Nov 7 2019

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference28th ACM International Conference on Information and Knowledge Management, CIKM 2019
CountryChina
CityBeijing
Period11/3/1911/7/19

Fingerprint

Convolution
Graph
Benchmark
Recommender systems
Nonlinear relationships
Filter
Dimensionality reduction

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

Cite this

Liu, X., He, J., Duddy, S., & O'Sullivan, L. (2019). Convolution-consistent collective matrix completion. In CIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management (pp. 2209-2212). (International Conference on Information and Knowledge Management, Proceedings). Association for Computing Machinery. https://doi.org/10.1145/3357384.3358111

Convolution-consistent collective matrix completion. / Liu, Xu; He, Jingrui; Duddy, Sam; O'Sullivan, Liz.

CIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, 2019. p. 2209-2212 (International Conference on Information and Knowledge Management, Proceedings).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Liu, X, He, J, Duddy, S & O'Sullivan, L 2019, Convolution-consistent collective matrix completion. in CIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management. International Conference on Information and Knowledge Management, Proceedings, Association for Computing Machinery, pp. 2209-2212, 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, Beijing, China, 11/3/19. https://doi.org/10.1145/3357384.3358111
Liu X, He J, Duddy S, O'Sullivan L. Convolution-consistent collective matrix completion. In CIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management. Association for Computing Machinery. 2019. p. 2209-2212. (International Conference on Information and Knowledge Management, Proceedings). https://doi.org/10.1145/3357384.3358111
Liu, Xu ; He, Jingrui ; Duddy, Sam ; O'Sullivan, Liz. / Convolution-consistent collective matrix completion. CIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, 2019. pp. 2209-2212 (International Conference on Information and Knowledge Management, Proceedings).
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