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User Churn Migration Analysis with DEDICOM

Authors:

Rafet Sifa,

César Ojeda,

Christian BauckhageAuthors Info & Claims

RecSys '15: Proceedings of the 9th ACM Conference on Recommender Systems

Pages 321 - 324

https://doi.org/10.1145/2792838.2799680

Published: 16 September 2015 Publication History

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Abstract

Time plays an important role regarding user preferences for products. It introduces asymmetries into the adoption of products which should be considered in the context of recommender systems and business intelligence. We therefore investigate how temporally asymmetric user preferences can be analyzed using a latent factor model called Decomposition Into Directional Components (DEDICOM). We introduce a new scalable hybrid algorithm that combines projected gradient descent and alternating least squares updates to compute DEDICOM and imposes semi-nonnegativity constraints to better interpret the resulting factors. We apply our model to analyze user churn and migration between different computer games in a social gaming environment.

References

[1]

B. Bader, R. Harshman, and T. Kolda. Temporal Analysis of Semantic Graphs using ASALSAN. In Proc. IEEE ICDM, 2007.

Digital Library

Google Scholar

[2]

C. Bauckhage, R. Sifa, A. Drachen, C. Thurau, and F. Hadiji. Beyond Heatmaps: Spatio-Temporal Clustering using Behavior-Based Partitioning of Game Levels. In Proc. IEEE CIG, 2014.

Crossref

Google Scholar

[3]

R. Bro and S. De Jong. A Fast Non-negativity constrained Least Squares Algorithm. J. of Chemometrics, 11(5), 1997.

Google Scholar

[4]

P. A. Chew, B. W. Bader, and A. Rozovskaya. Using DEDICOM for Completely Unsupervised Part Of Speech Tagging. In Proc. Workshop on Unsupervised and Minimally Supervised Learning of Lexical Semantics, 2009.

Digital Library

Google Scholar

[5]

F. Hadiji, R. Sifa, A. Drachen, C. Thurau, K. Kersting, and C. Bauckhage. Predicting Player Churn in the Wild. In Proc. IEEE CIG, 2014.

Crossref

Google Scholar

[6]

R. Harshman. Models for Analysis of Asymmetrical Relationships among N Objects or Stimuli. In Proc. Joint Meeting of the Psychometric Society and the Society for Mathematical Psychology, 1978.

Google Scholar

[7]

H. Kiers. DESICOM: Decomposition of Asymmetric Relationships Data into Simple Components. Behaviormetrika, 24(2), 1997.

Google Scholar

[8]

N. Koenigstein and Y. Koren. Towards Scalable and Accurate Item-oriented Recommendations. In Proc. ACM RecSys, 2013.

Digital Library

Google Scholar

[9]

Y. Koren, R. Bell, and C. Volinsky. Matrix Factorization Techniques for Recommender Systems. IEEE Computer, 42(8), 2009.

Digital Library

Google Scholar

[10]

C. L. Lawson and R. J. Hanson. Solving Least Squares Problems. SIAM, 1974.

Google Scholar

[11]

J. Runge, P. Gao, F. Garcin, and B. Faltings. Churn Prediction for High-value Players in Casual Social Games. In Proc. IEEE CIG, 2014.

Crossref

Google Scholar

Cited By

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Hillebrand LBiesner DBauckhage CSifa R(2020)Interpretable Topic Extraction and Word Embedding Learning Using Row-Stochastic DEDICOMMachine Learning and Knowledge Extraction10.1007/978-3-030-57321-8_22(401-422)Online publication date: 2020
https://doi.org/10.1007/978-3-030-57321-8_22
Sifa R(2020)DESICOM as Metaheuristic SearchLearning and Intelligent Optimization10.1007/978-3-030-53552-0_38(421-427)Online publication date: 18-Jul-2020
https://doi.org/10.1007/978-3-030-53552-0_38
Aung MDemediuk SSun YTu YAng YNekkanti SRaghav SKlabjan DSifa RDrachen ADeterding SKhosmood FPirker JApperley T(2019)The trails of Just Cause 2Proceedings of the 14th International Conference on the Foundations of Digital Games10.1145/3337722.3337765(1-11)Online publication date: 26-Aug-2019
https://dl.acm.org/doi/10.1145/3337722.3337765
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Index Terms

User Churn Migration Analysis with DEDICOM
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

RecSys '15: Proceedings of the 9th ACM Conference on Recommender Systems

September 2015

414 pages

ISBN:9781450336925

DOI:10.1145/2792838

General Chairs:
Hannes Werthner
TU Wien, Austria
,
Markus Zanker
Alpen-Adria-Universität Klagenfurt, Austria
,
Program Chairs:
Jennifer Golbeck
University of Maryland, USA
,
Giovanni Semeraro
University of Bari Aldo Moro, Italy

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 the author(s) 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: 16 September 2015

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RecSys '15: Ninth ACM Conference on Recommender Systems

September 16 - 20, 2015

Vienna, Austria

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RecSys '15 Paper Acceptance Rate 28 of 131 submissions, 21%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Hillebrand LBiesner DBauckhage CSifa R(2020)Interpretable Topic Extraction and Word Embedding Learning Using Row-Stochastic DEDICOMMachine Learning and Knowledge Extraction10.1007/978-3-030-57321-8_22(401-422)Online publication date: 2020
https://doi.org/10.1007/978-3-030-57321-8_22
Sifa R(2020)DESICOM as Metaheuristic SearchLearning and Intelligent Optimization10.1007/978-3-030-53552-0_38(421-427)Online publication date: 18-Jul-2020
https://doi.org/10.1007/978-3-030-53552-0_38
Aung MDemediuk SSun YTu YAng YNekkanti SRaghav SKlabjan DSifa RDrachen ADeterding SKhosmood FPirker JApperley T(2019)The trails of Just Cause 2Proceedings of the 14th International Conference on the Foundations of Digital Games10.1145/3337722.3337765(1-11)Online publication date: 26-Aug-2019
https://dl.acm.org/doi/10.1145/3337722.3337765
Sifa RYawar RRamamurthy RBauckhage CKersting K(2019)Matrix- and Tensor Factorization for Game Content RecommendationKI - Künstliche Intelligenz10.1007/s13218-019-00620-2Online publication date: 13-Sep-2019
https://doi.org/10.1007/s13218-019-00620-2
Sifa RPawlakos EZhai KHaran SJha RKlabjan DDrachen AAbramson D(2018)Controlling the crucibleProceedings of the Australasian Computer Science Week Multiconference10.1145/3167918.3167926(1-10)Online publication date: 29-Jan-2018
https://dl.acm.org/doi/10.1145/3167918.3167926

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Utilizing Textual Reviews for Visualizing and Understanding User Preferences

Improving Total Migration Time in Live Virtual Machine Migration

Exploiting user and item embedding in latent factor models for recommendations

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