Cited By
View all- Beel JBreitinger CLanger SLommatzsch AGipp B(2016)Towards reproducibility in recommender-systems researchUser Modeling and User-Adapted Interaction10.1007/s11257-016-9174-x26:1(69-101)Online publication date: 1-Mar-2016
FReSET: an evaluation framework for folksonomy-based recommender systems
Authors: 
Renato Domínguez García, Matthias Bender, Mojisola Anjorin, 
Christoph Rensing, Ralf SteinmetzAuthors Info & Claims
Renato Domínguez García, Matthias Bender, Mojisola Anjorin, Christoph Rensing, Ralf SteinmetzAuthors Info & ClaimsPages 25 - 28
Abstract
FReSET is a new recommender systems evaluation framework aiming to support research on folksonomy-based recommender systems. It provides interfaces for the implementation of folksonomy-based recommender systems and supports the consistent and reproducible offline evaluations on historical data. Unlike other recommender systems framework projects, the emphasis here is on providing a flexible framework allowing users to implement their own folksonomy-based recommender algorithms and pre-processing filtering methods rather than just providing a collection of collaborative filtering implementations. FReSET includes a graphical interface for result visualization and different cross-validation implementations to complement the basic functionality.
References
[1]
D. Bohnstedt, L. Lehmann, C. Rensing, and R. Steinmetz. Automatic identification of tag types in a resource-based learning scenario. In Towards Ubiquitous Learning, volume 6964 of LNCS, pages 57--70. Springer Berlin, 2011.
[2]
M. D. Ekstrand, M. Ludwig, J. Kolb, and J. Riedl. Lenskit: a modular recommender framework. In B. Mobasher, R. D. Burke, D. Jannach, and G. Adomavicius, editors, RecSys. ACM, 2011.
[3]
M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The weka data mining software: an update. SIGKDD Explor. Newsl., 11(1):10--18, 2009.
[4]
R. Jaschke, L. Marinho, A. Hotho, L. Schmidt-Thieme, and G. Stumme. Tag recommendations in folksonomies. In J. Kok, J. Koronacki, R. Lopez de Mantaras, S. Matwin, D. Mladenic, and A. Skowron, editors, Knowledge Discovery in Databases: PKDD 2007, volume 4702 of LNCS, pages 506--514. Springer, 2007.
[5]
L. Liu and M. T. Zsu. Encyclopedia of Database Systems. Springer Publishing Company, Incorporated, 1st edition, 2009.
[6]
L. B. Marinho, A. Hotho, R. Jaschke, A. Nanopoulos, S. Rendle, L. Schmidt-Thieme, G. Stumme, and P. Symeonidis. Social tagging systems. In Recommender Systems for Social Tagging Systems, Springer, pages 3--15. Springer US, 2012.
Index Terms
- FReSET: an evaluation framework for folksonomy-based recommender systems
Recommendations
Exploiting Social Tagging in a Web 2.0 Recommender System
Recommender systems help users cope with information overload by using their preferences to recommend items. To date, most recommenders have employed users' ratings, information about the user's profile, or metadata describing the items. To take ...
Personalized recommender system based on item taxonomy and folksonomy
CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge managementItem folksonomy or tag information is popularly available on the web now. However, since tags are arbitrary words given by users, they contain a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise brings difficulties to ...
Tagommenders: connecting users to items through tags
WWW '09: Proceedings of the 18th international conference on World wide webTagging has emerged as a powerful mechanism that enables users to find, organize, and understand online entities. Recommender systems similarly enable users to efficiently navigate vast collections of items. Algorithms combining tags with recommenders ...
Comments
Information & Contributors
Information
Published In
September 2012
68 pages
ISBN:9781450316385
DOI:10.1145/2365934
- Program Chairs:
- Bamshad Mobasher,
- Dietmar Jannach,
- Werner Geyer,
- Andreas Hotho
Copyright © 2012 ACM.
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]
Sponsors
In-Cooperation
- SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
- SIGAI: ACM Special Interest Group on Artificial Intelligence
- SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
- SIGIR: ACM Special Interest Group on Information Retrieval
- SIGecom: Special Interest Group on Economics and Computation
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 09 September 2012
Check for updates
Author Tags
Qualifiers
- Research-article
Conference
RecSys '12
Sponsor:
Acceptance Rates
RSWeb '12 Paper Acceptance Rate 8 of 13 submissions, 62%;
Overall Acceptance Rate 8 of 13 submissions, 62%
Upcoming Conference
CHI 2025
- Sponsor:
- sigchi
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 207Total Downloads
- Downloads (Last 12 months)5
- Downloads (Last 6 weeks)1
Reflects downloads up to 25 Feb 2025
Other Metrics
Citations
Cited By
View all- Beel JBreitinger CLanger SLommatzsch AGipp B(2016)Towards reproducibility in recommender-systems researchUser Modeling and User-Adapted Interaction10.1007/s11257-016-9174-x26:1(69-101)Online publication date: 1-Mar-2016
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in