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Report on RecSys 2015 Workshop on New Trends in Content-Based Recommender Systems

Authors:

Toine Bogers and

Marijn KoolenAuthors Info & Claims

ACM SIGIR Forum, Volume 49, Issue 2

Pages 141 - 146

https://doi.org/10.1145/2888422.2888445

Published: 29 January 2016 Publication History

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Abstract

This article reports on the CBRecSys 2015 workshop, the second edition of the workshop on new trends in content-based recommender systems, co-located with RecSys 2015 in Vienna, Austria. Content-based recommendation has been applied successfully in many different domains, but it has not seen the same level of attention as collaborative filtering techniques have. Nevertheless, there are many recommendation domains and applications where content and metadata play a key role, either in addition to or instead of ratings and implicit usage data. The CBRecSys workshop series provides a dedicated venue for work dedicated to all aspects of content-based recommender systems.

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Cited By

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Kim KChang DChoi Y(2019)Boosting Memory-Based Collaborative Filtering Using Content-MetadataSymmetry10.3390/sym1104056111:4(561)Online publication date: 18-Apr-2019
https://doi.org/10.3390/sym11040561
Bogers TKoolen MMusto CLops PSemeraro G(2017)Report on RecSys 2016Workshop on New Trends in Content-Based Recommender SystemsACM SIGIR Forum10.1145/3130332.313034151:1(45-51)Online publication date: 2-Aug-2017
https://dl.acm.org/doi/10.1145/3130332.3130341
Bogers TKoolen MMusto CLops PSemeraro GSen SGeyer WFreyne JCastells P(2016)Third Workshop on New Trends in Content-based Recommender Systems (CBRecSys 2016)Proceedings of the 10th ACM Conference on Recommender Systems10.1145/2959100.2959200(419-420)Online publication date: 7-Sep-2016
https://dl.acm.org/doi/10.1145/2959100.2959200
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Index Terms

Report on RecSys 2015 Workshop on New Trends in Content-Based Recommender Systems
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

Index terms have been assigned to the content through auto-classification.

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Report on RecSys 2014: Workshop on New Trends in Content-Based Recommender Systems

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Information & Contributors

Information

Published In

ACM SIGIR Forum Volume 49, Issue 2

December 2015

141 pages

ISSN:0163-5840

DOI:10.1145/2888422

Editors:
Ben Carterette
University of Delaware, Newark, DE, USA
,
Craig Macdonald
University of Glasgow, Glasgow, UK

Issue’s Table of Contents

Publisher

Association for Computing Machinery

New York, NY, United States

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Published: 29 January 2016

Published in SIGIR Volume 49, Issue 2

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Kim KChang DChoi Y(2019)Boosting Memory-Based Collaborative Filtering Using Content-MetadataSymmetry10.3390/sym1104056111:4(561)Online publication date: 18-Apr-2019
https://doi.org/10.3390/sym11040561
Bogers TKoolen MMusto CLops PSemeraro G(2017)Report on RecSys 2016Workshop on New Trends in Content-Based Recommender SystemsACM SIGIR Forum10.1145/3130332.313034151:1(45-51)Online publication date: 2-Aug-2017
https://dl.acm.org/doi/10.1145/3130332.3130341
Bogers TKoolen MMusto CLops PSemeraro GSen SGeyer WFreyne JCastells P(2016)Third Workshop on New Trends in Content-based Recommender Systems (CBRecSys 2016)Proceedings of the 10th ACM Conference on Recommender Systems10.1145/2959100.2959200(419-420)Online publication date: 7-Sep-2016
https://dl.acm.org/doi/10.1145/2959100.2959200
Liu XWang GJiang WLong Y(2016)DHMRF: A Dynamic Hybrid Movie Recommender FrameworkAdvances in Services Computing10.1007/978-3-319-49178-3_37(491-503)Online publication date: 10-Nov-2016
https://doi.org/10.1007/978-3-319-49178-3_37

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Report on RecSys 2014: Workshop on New Trends in Content-Based Recommender Systems

Report on RecSys 2016Workshop on New Trends in Content-Based Recommender Systems

Second Workshop on New Trends in Content-based Recommender Systems (CBRecSys 2015)

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Report on RecSys 2014: Workshop on New Trends in Content-Based Recommender Systems

Report on RecSys 2016Workshop on New Trends in Content-Based Recommender Systems

Second Workshop on New Trends in Content-based Recommender Systems (CBRecSys 2015)

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