poster

Data imputation using a trust network for recommendation

Authors:

Won-Seok Hwang,

Shaoyu Li,

Sang-Wook Kim,

Kichun LeeAuthors Info & Claims

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

Pages 299 - 300

https://doi.org/10.1145/2567948.2577363

Published: 07 April 2014 Publication History

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Abstract

Recommendation methods suffer from the data sparsity and cold-start user problems, often resulting in low accuracy. To address these problems, we propose a novel imputation method, which effectively densifies a rating matrix by filling unevaluated ratings with probable values. In our method, we use a trust network to estimate the unevaluated ratings accurately. We conduct experiments on the Epinions dataset and demonstrate that our method helps provide better recommendation accuracy than previous methods, especially for cold-start users.

References

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Cited By

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Alghamedy FZhang J(2019)Imputation Strategies for Cold-Start Users in NMF-Based Recommendation SystemsProceedings of the 2019 3rd International Conference on Information System and Data Mining10.1145/3325917.3325933(119-128)Online publication date: 6-Apr-2019
https://dl.acm.org/doi/10.1145/3325917.3325933
Jiang LShi LLiu LYao JAli M(2019)User interest community detection on social media using collaborative filteringWireless Networks10.1007/s11276-018-01913-428:3(1169-1175)Online publication date: 27-Feb-2019
https://doi.org/10.1007/s11276-018-01913-4
Alghamedy FWang XZhang JWong KShen CBrown D(2018)Imputing trust network information in NMF-based collaborative filteringProceedings of the 2018 ACM Southeast Conference10.1145/3190645.3190672(1-8)Online publication date: 29-Mar-2018
https://dl.acm.org/doi/10.1145/3190645.3190672
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Index Terms

Data imputation using a trust network for recommendation
1. Information systems
  1. Information systems applications
    1. Data mining

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Information & Contributors

Information

Published In

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

April 2014

1396 pages

ISBN:9781450327459

DOI:10.1145/2567948

General Chair:
Chin-Wan Chung
Korea Advanced Institute of Science and Technology, Korea
,
Program Chairs:
Andrei Broder
Google Inc., USA
,
Kyuseok Shim
Seoul National University, Korea
,
Torsten Suel
New York University, USA

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 April 2014

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Poster

Funding Sources

National IT Industry Promotion Agency

Conference

WWW '14

Sponsor:

IW3C2

WWW '14: 23rd International World Wide Web Conference

April 7 - 11, 2014

Seoul, Korea

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

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Alghamedy FZhang J(2019)Imputation Strategies for Cold-Start Users in NMF-Based Recommendation SystemsProceedings of the 2019 3rd International Conference on Information System and Data Mining10.1145/3325917.3325933(119-128)Online publication date: 6-Apr-2019
https://dl.acm.org/doi/10.1145/3325917.3325933
Jiang LShi LLiu LYao JAli M(2019)User interest community detection on social media using collaborative filteringWireless Networks10.1007/s11276-018-01913-428:3(1169-1175)Online publication date: 27-Feb-2019
https://doi.org/10.1007/s11276-018-01913-4
Alghamedy FWang XZhang JWong KShen CBrown D(2018)Imputing trust network information in NMF-based collaborative filteringProceedings of the 2018 ACM Southeast Conference10.1145/3190645.3190672(1-8)Online publication date: 29-Mar-2018
https://dl.acm.org/doi/10.1145/3190645.3190672
Lee YKim SPark SXie XChampin PGandon FMédini LLalmas MIpeirotis P(2018)How to Impute Missing Ratings?Proceedings of the 2018 World Wide Web Conference10.1145/3178876.3186159(783-792)Online publication date: 10-Apr-2018
https://dl.acm.org/doi/10.1145/3178876.3186159
Jo YJang MKim SHan K(2018)Efficient processing of recommendation algorithms on a single-machine-based graph engineThe Journal of Supercomputing10.1007/s11227-018-2477-4Online publication date: 10-Jul-2018
https://doi.org/10.1007/s11227-018-2477-4

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Recommendations

Improving Top-N Recommendation for Cold-Start Users via Cross-Domain Information

An effective recommendation method for cold start new users using trust and distrust networks

Strength of social influence in trust networks in product review sites