research-article

Predicting who rated what in large-scale datasets

Authors:

Yan Liu,

Zhenzhen KouAuthors Info & Claims

ACM SIGKDD Explorations Newsletter, Volume 9, Issue 2

Pages 62 - 65

https://doi.org/10.1145/1345448.1345462

Published: 01 December 2007 Publication History

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Abstract

KDD Cup 2007 focuses on movie rating behaviors. The goal of the task "Who Rated What" is to predict whether "existing" users will review "existing" movies in the future. We cast the task as a link prediction problem and address it via a simple classification approach. Compared with other applications for link prediction, there are two major challenges in our task: (1) the huge size of the Netflix data; (2) the prediction target is complicated by many factors, such as a general decrease of interest in old movies and more tendency to review more movies by Netflix users due to the success of the internet DVD rental industries. We address the first challenge by "selective" subsampling and the second by combining information from the review scores, movie contents and graph topology effectively.

References

[1]

M. Berry, S. Dumais, and T. Letsche. Computational methods for intelligent access. In Proc. of Supercomputing'95, 1995.

Digital Library

Google Scholar

[2]

G. Golub and C. V. Loan. Matrix Computations. Johns-Hopkins, Baltimore, second ed., 1989.

Google Scholar

[3]

M. Hasan, V. Chaoji, S. Salem, and M. J. Zaki. Link prediction using supervised learning. In Proc. of SDM 06 workshop on Link Analysis, Counterrorism and Security, 2006.

Google Scholar

[4]

T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer; 1st ed., 2001.

Google Scholar

[5]

Z. Huang. Link prediction based on graph topology: The predictive value of generalized clustering coefficient. In Proc. of KDD 06 workshop on Link Analysis: Dynamics and Static of Large Networks (LinkKDD2006), 2006.

Google Scholar

[6]

H. Kashima and N. Abe. A parameterized probabilistic model of network evolution for supervised link prediction. In Proc. of the 2006 IEEE International Conference on Data Mining (ICDM 2006), 2006.

Digital Library

Google Scholar

[7]

D. Liben-Nowell and J. Kleinberg. The link prediction problem for social networks. In Proc. of 12th International Conference on Information and Knowledge Management (CIKM), 2003.

Digital Library

Google Scholar

[8]

B. Taskar, M.-F. Wong, P. Abbeel, and D. Koller. Link prediction in relational data. In Proc. of Advances in Neural Information Processing Systems (NIPS 2003), 2003.

Google Scholar

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Kosztyán ZKirály FKatona ACsizmadia TFehérvölgyi B(2024)Analysis and prediction of the Horizon 2020 R&D&I collaboration networkExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124417255:PBOnline publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.124417
Kombo VAsbaş CTuzlukaya Ş(2023)Artificial Intelligence, Dynamic Capabilities, and Business OptimizationStreamlining Organizational Processes Through AI, IoT, Blockchain, and Virtual Environments10.4018/978-1-6684-8639-9.ch001(1-20)Online publication date: 30-Jun-2023
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Lv LBardou DHu PLiu YYu G(2022)Graph regularized nonnegative matrix factorization for link prediction in directed temporal networks using PageRank centralityChaos, Solitons & Fractals10.1016/j.chaos.2022.112107159(112107)Online publication date: Jun-2022
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Predicting who rated what in large-scale datasets
1. Information systems
  1. Information systems applications

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Published In

ACM SIGKDD Explorations Newsletter Volume 9, Issue 2

Special issue on visual analytics

December 2007

105 pages

ISSN:1931-0145

EISSN:1931-0153

DOI:10.1145/1345448

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Publisher

Association for Computing Machinery

New York, NY, United States

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Published: 01 December 2007

Published in SIGKDD Volume 9, Issue 2

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Kosztyán ZKirály FKatona ACsizmadia TFehérvölgyi B(2024)Analysis and prediction of the Horizon 2020 R&D&I collaboration networkExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124417255:PBOnline publication date: 18-Oct-2024
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Kombo VAsbaş CTuzlukaya Ş(2023)Artificial Intelligence, Dynamic Capabilities, and Business OptimizationStreamlining Organizational Processes Through AI, IoT, Blockchain, and Virtual Environments10.4018/978-1-6684-8639-9.ch001(1-20)Online publication date: 30-Jun-2023
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Raut PKhandelwal HVyas G(2020)A Comparative Study of Classification Algorithms for Link Prediction2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)10.1109/ICIMIA48430.2020.9074840(479-483)Online publication date: Mar-2020
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Gao HKuang LYin YGuo BDou K(2020)Mining consuming Behaviors with Temporal Evolution for Personalized Recommendation in Mobile Marketing AppsMobile Networks and Applications10.1007/s11036-020-01535-1Online publication date: 6-Jun-2020
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Makarov IGerasimova OSulimov PZhukov L(2019)Dual network embedding for representing research interests in the link prediction problem on co-authorship networksPeerJ Computer Science10.7717/peerj-cs.1725(e172)Online publication date: 21-Jan-2019
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Time-Dependent Models in Collaborative Filtering Based Recommender System

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