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Applying multi-view based metadata in personalized ranking for recommender systems

Authors:

Marcos A. Domingues,

Camila V. Sundermann,

Flávio M. M. Barros,

Marcelo G. Manzato,

Maria G. C. Pimentel,

Solange O. Rezende,

Stanley OliveiraAuthors Info & Claims

SAC '15: Proceedings of the 30th Annual ACM Symposium on Applied Computing

Pages 1105 - 1107

https://doi.org/10.1145/2695664.2695955

Published: 13 April 2015 Publication History

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Abstract

In this paper, we propose a multi-view based metadata extraction technique from unstructured textual content in order to be applied in recommendation algorithms based on latent factors. The solution aims at reducing the problem of intense and time-consuming human effort to identify, collect and label descriptions about the items. Our proposal uses a unsupervised learning method to construct topic hierarchies with named entity recognition as privileged information. We evaluate the technique using different recommendation algorithms, and show that better accuracy is obtained when additional information about items is considered.

References

[1]

Z. Gantner, L. Drumond, C. Freudenthaler, S. Rendle, and L. Schmidt-Thieme. Learning attribute-to-feature mappings for cold-start recommendations. In ICDM '10, pages 176--185, 2010.

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[2]

M. G. Manzato, M. A. Domingues, R. M. Marcacini, and S. O. Rezende. Improving personalized ranking in recommender systems with topic hierarchies and implicit feedback. In ICPR '14, pages 3696--3701, 2014.

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[3]

R. Marcacini and S. O. Rezende. Incremental Hierarchical Text Clustering with Privileged Information. In DocEng '13, pages 231--232, 2013.

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Google Scholar

[4]

S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. BPR: Bayesian personalized ranking from implicit feedback. In UAI '09, pages 452--461, 2009.

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F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, editors. Recommender Systems Handbook. Springer, 2011.

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Cited By

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D’Addio RDomingues MManzato M(2017)Exploiting feature extraction techniques on users’ reviews for movies recommendationJournal of the Brazilian Computer Society10.1186/s13173-017-0057-823:1Online publication date: 5-Jun-2017
https://doi.org/10.1186/s13173-017-0057-8
McGarry GTolmie PBenford SGreenhalgh CChamberlain ALee CPoltrock SBarkhuus LBorges MKellogg W(2017)"They're all going out to something weird"Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing10.1145/2998181.2998325(995-1008)Online publication date: 25-Feb-2017
https://dl.acm.org/doi/10.1145/2998181.2998325
Palomares IKovalchuk S(2017)Multi-View Data approaches in Recommender SystemsProcedia Computer Science10.1016/j.procs.2017.11.157119:C(30-41)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1016/j.procs.2017.11.157
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Index Terms

Applying multi-view based metadata in personalized ranking for recommender systems
1. Information systems
  1. Information retrieval
    1. Document representation
    2. Search engine architectures and scalability
      1. Search engine indexing

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Information

Published In

SAC '15: Proceedings of the 30th Annual ACM Symposium on Applied Computing

April 2015

2418 pages

ISBN:9781450331968

DOI:10.1145/2695664

Conference Chairs:
Roger L. Wainwright
University of Tulsa
,
Juan Manuel Corchado
University of Salamanca, Spain
,
Program Chairs:
Alessio Bechini
University of Pisa, Italy
,
Jiman Hong
Soongsil University, South Korea

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 April 2015

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SAC 2015: Symposium on Applied Computing

April 13 - 17, 2015

Salamanca, Spain

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SAC '15 Paper Acceptance Rate 291 of 1,211 submissions, 24%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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D’Addio RDomingues MManzato M(2017)Exploiting feature extraction techniques on users’ reviews for movies recommendationJournal of the Brazilian Computer Society10.1186/s13173-017-0057-823:1Online publication date: 5-Jun-2017
McGarry GTolmie PBenford SGreenhalgh CChamberlain ALee CPoltrock SBarkhuus LBorges MKellogg W(2017)"They're all going out to something weird"Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing10.1145/2998181.2998325(995-1008)Online publication date: 25-Feb-2017
Palomares IKovalchuk S(2017)Multi-View Data approaches in Recommender SystemsProcedia Computer Science10.1016/j.procs.2017.11.157119:C(30-41)Online publication date: 1-Dec-2017
Lee CPoltrock SBarkhuus LBorges MKellogg W(2017)Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social ComputingundefinedOnline publication date: 25-Feb-2017
Manzato MDomingues MFortes ASundermann CD’Addio RConrado MRezende SPimentel M(2016)Mining unstructured content for recommender systems: an ensemble approachInformation Retrieval Journal10.1007/s10791-016-9280-819:4(378-415)Online publication date: 24-May-2016

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Recommendations

Serendipitous Personalized Ranking for Top-N Recommendation

Learning from Sets of Items in Recommender Systems

Correcting noisy ratings in collaborative recommender systems

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