Article

Predictive discrete latent factor models for large scale dyadic data

Authors:

Deepak Agarwal,

Srujana MeruguAuthors Info & Claims

KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 26 - 35

https://doi.org/10.1145/1281192.1281199

Published: 12 August 2007 Publication History

Abstract

We propose a novel statistical method to predict large scale dyadic response variables in the presence of covariate information. Our approach simultaneously incorporates the effect of covariates and estimates local structure that is induced by interactions among the dyads through a discrete latent factor model. The discovered latent factors provide a redictive model that is both accurate and interpretable. We illustrate our method by working in a framework of generalized linear models, which include commonly used regression techniques like linear regression, logistic regression and Poisson regression as special cases. We also provide scalable generalized EM-based algorithms for model fitting using both "hard" and "soft" cluster assignments. We demonstrate the generality and efficacy of our approach through large scale simulation studies and analysis of datasets obtained from certain real-world movie recommendation and internet advertising applications.

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Cited By

Boutalbi RLabiod LNadif M(2023)Latent Block Regression ModelClassification and Data Science in the Digital Age10.1007/978-3-031-09034-9_9(73-81)Online publication date: 8-Dec-2023
https://doi.org/10.1007/978-3-031-09034-9_9
Liu ZWang HChen WWang LLi T(2022)Bilateral discriminative autoencoder model orienting co-representation learningKnowledge-Based Systems10.1016/j.knosys.2022.108653245(108653)Online publication date: Jun-2022
https://doi.org/10.1016/j.knosys.2022.108653
Yang WWu LLiu XFan C(2018)MFCC: An Efficient and Effective Matrix Factorization Model Based on Co-clusteringInternet Multimedia Computing and Service10.1007/978-981-10-8530-7_35(360-370)Online publication date: 1-Mar-2018
https://doi.org/10.1007/978-981-10-8530-7_35
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Index Terms

Predictive discrete latent factor models for large scale dyadic data
1. Computing methodologies
  1. Modeling and simulation
    1. Simulation theory
      1. Systems theory
2. Mathematics of computing
  1. Information theory

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Published In

cover image ACM Conferences

KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2007

1080 pages

ISBN:9781595936097

DOI:10.1145/1281192

General Chair:
Pavel Berkhin
Yahoo!, USA
,
Program Chairs:
Rich Caruana
Cornell University, USA
,
Xindong Wu
University of Vermont, USA

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 12 August 2007

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KDD07: The 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 12 - 15, 2007

California, San Jose, USA

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KDD '07 Paper Acceptance Rate 111 of 573 submissions, 19%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Boutalbi RLabiod LNadif M(2023)Latent Block Regression ModelClassification and Data Science in the Digital Age10.1007/978-3-031-09034-9_9(73-81)Online publication date: 8-Dec-2023
https://doi.org/10.1007/978-3-031-09034-9_9
Liu ZWang HChen WWang LLi T(2022)Bilateral discriminative autoencoder model orienting co-representation learningKnowledge-Based Systems10.1016/j.knosys.2022.108653245(108653)Online publication date: Jun-2022
https://doi.org/10.1016/j.knosys.2022.108653
Yang WWu LLiu XFan C(2018)MFCC: An Efficient and Effective Matrix Factorization Model Based on Co-clusteringInternet Multimedia Computing and Service10.1007/978-981-10-8530-7_35(360-370)Online publication date: 1-Mar-2018
https://doi.org/10.1007/978-981-10-8530-7_35
Deodhar MGhosh JSaar-Tsechansky MKeshari V(2017)Active Learning with Multiple Localized Regression ModelsINFORMS Journal on Computing10.1287/ijoc.2016.073229:3(503-522)Online publication date: Aug-2017
https://doi.org/10.1287/ijoc.2016.0732
Anggistia MSaefuddin ASartono B(2017)Simultaneous Co-Clustering and Classification in Customers InsightJournal of Physics: Conference Series10.1088/1742-6596/824/1/012033824(012033)Online publication date: 18-Apr-2017
https://doi.org/10.1088/1742-6596/824/1/012033
Yang LHuang WNiu X(2017)Defending shilling attacks in recommender systems using soft co-clusteringIET Information Security10.1049/iet-ifs.2016.034511:6(319-325)Online publication date: 1-Nov-2017
https://doi.org/10.1049/iet-ifs.2016.0345
Wang T(2017)A group interest-based collaborative filtering algorithm for multimedia informationMultimedia Tools and Applications10.1007/s11042-017-5516-x77:4(4401-4415)Online publication date: 24-Dec-2017
https://doi.org/10.1007/s11042-017-5516-x
Xu YYu QLam WLin T(2017)Exploiting interactions of review text, hidden user communities and item groups, and time for collaborative filteringKnowledge and Information Systems10.1007/s10115-016-1005-152:1(221-254)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1007/s10115-016-1005-1
Huang SWang HLi TYang YLi T(2016)Constraint Co-Projections for Semi-Supervised Co-ClusteringIEEE Transactions on Cybernetics10.1109/TCYB.2015.249617446:12(3047-3058)Online publication date: Dec-2016
https://doi.org/10.1109/TCYB.2015.2496174
Wang FWang GLin SYu P(2015)Concurrent goal-oriented co-clustering generation in social networksProceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)10.1109/ICOSC.2015.7050833(350-357)Online publication date: Feb-2015
https://doi.org/10.1109/ICOSC.2015.7050833
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