research-article

Understanding the Sparsity: Augmented Matrix Factorization with Sampled Constraints on Unobservables

Authors:

Yongfeng Zhang,

Shaoping MaAuthors Info & Claims

CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

Pages 1189 - 1198

https://doi.org/10.1145/2661829.2661976

Published: 03 November 2014 Publication History

Abstract

An important problem of matrix completion/approximation based on Matrix Factorization (MF) algorithms is the existence of multiple global optima; this problem is especially serious when the matrix is sparse, which is common in real-world applications such as personalized recommender systems. In this work, we clarify data sparsity by bounding the solution space of MF algorithms. We present the conditions that an MF algorithm should satisfy for reliable completion of the unobservables, and we further propose to augment current MF algorithms with extra constraints constructed by compressive sampling on the unobserved values, which is well-motivated by the theoretical analysis. Model learning and optimal solution searching is conducted in a properly reduced solution space to achieve more accurate and efficient rating prediction performances. We implemented the proposed algorithms in the Map-Reduce framework, and comprehensive experimental results on Yelp and Dianping datasets verified the effectiveness and efficiency of the augmented matrix factorization algorithms.

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Cited By

Tang GGan XWang JLu BWu LFu LZhou CHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)EditKG: Editing Knowledge Graph for RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657723(112-122)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657723
Zhang YChen XZhang YChen XDiaz FShah CSuel TCastells PJones RSakai T(2021)CSR 2021: The 1st International Workshop on Causality in Search and RecommendationProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462817(2677-2680)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3462817
Tan YZhang MLiu YMa S(2016)Rating-boosted latent topicsProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence10.5555/3060832.3060990(2640-2646)Online publication date: 9-Jul-2016
https://dl.acm.org/doi/10.5555/3060832.3060990
Show More Cited By

Index Terms

Understanding the Sparsity: Augmented Matrix Factorization with Sampled Constraints on Unobservables
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
2. Mathematics of computing
  1. Mathematical analysis
    1. Numerical analysis
      1. Computations on matrices

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cover image ACM Conferences

CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

November 2014

2152 pages

ISBN:9781450325981

DOI:10.1145/2661829

General Chairs:
Jianzhong Li
Harbin Inst. of Technology
,
X. Sean Wang
Fudan University
,
Program Chairs:
Minos Garofalakis
Technical University of Crete, Greece
,
Ian Soboroff
National Institute of Standards, USA
,
Torsten Suel
New York University, USA
,
Min Wang
Google Research, USA

Copyright © 2014.

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Published: 03 November 2014

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November 3 - 7, 2014

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CIKM '14 Paper Acceptance Rate 175 of 838 submissions, 21%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Tang GGan XWang JLu BWu LFu LZhou CHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)EditKG: Editing Knowledge Graph for RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657723(112-122)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657723
Zhang YChen XZhang YChen XDiaz FShah CSuel TCastells PJones RSakai T(2021)CSR 2021: The 1st International Workshop on Causality in Search and RecommendationProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462817(2677-2680)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3462817
Tan YZhang MLiu YMa S(2016)Rating-boosted latent topicsProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence10.5555/3060832.3060990(2640-2646)Online publication date: 9-Jul-2016
https://dl.acm.org/doi/10.5555/3060832.3060990
de Jesus FDorneles Cde Jesus Lima Gomes Fde Andrade Lira Rabelo Rde Salles Soares Neto CWillrich RCamillo Teixeira Cde Almeida Jde Carvalho W(2016)Weight Adjusment for Multi-criteria Ratings in Items RecommendationProceedings of the 22nd Brazilian Symposium on Multimedia and the Web10.1145/2976796.2976846(319-326)Online publication date: 8-Nov-2016
https://dl.acm.org/doi/10.1145/2976796.2976846
Zhang YZhao QZhang YFriedman DZhang MLiu YMa SBourdeau JHendler JNkambou RHorrocks IZhao B(2016)Economic Recommendation with Surplus MaximizationProceedings of the 25th International Conference on World Wide Web10.1145/2872427.2882973(73-83)Online publication date: 11-Apr-2016
https://dl.acm.org/doi/10.1145/2872427.2882973
Zhang QWang HBailey JMoffat AAggarwal Cde Rijke MKumar RMurdock VSellis TYu J(2015)Improving Collaborative Filtering via Hidden Structured ConstraintProceedings of the 24th ACM International on Conference on Information and Knowledge Management10.1145/2806416.2806623(1935-1938)Online publication date: 17-Oct-2015
https://dl.acm.org/doi/10.1145/2806416.2806623
Zhang YZhang MZhang YLai GLiu YZhang HMa SGangemi ALeonardi SPanconesi A(2015)Daily-Aware Personalized Recommendation based on Feature-Level Time Series AnalysisProceedings of the 24th International Conference on World Wide Web10.1145/2736277.2741087(1373-1383)Online publication date: 18-May-2015
https://dl.acm.org/doi/10.1145/2736277.2741087
Zhang YCheng XLi HGabrilovich ETang J(2015)Incorporating Phrase-level Sentiment Analysis on Textual Reviews for Personalized RecommendationProceedings of the Eighth ACM International Conference on Web Search and Data Mining10.1145/2684822.2697033(435-440)Online publication date: 2-Feb-2015
https://dl.acm.org/doi/10.1145/2684822.2697033

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