A Tag Recommendation Hybrid Model for Social Annotation Systems
DOI:
https://doi.org/10.29019/enfoqueute.v11n4.640Keywords:
Folksonomy, Social Tagging, User's tagging historyAbstract
Social tagging consists of classifying web resources using words or tags freely chosen by users. The simplicity and openness of social tagging systems to organize resources is the key to your success on the internet. There are numerous approaches to facilitate the user the labeling process, allowing them to reuse labels and thus optimizing their limited reading and writing time. This document proposes a different hybrid approach that simply solves the problem of recommendations based solely on the content of the resource, merging the list of recommendations with the most popular tags in the user's tag history, thus allowing them to reuse terms assigned to others resources.
Downloads
References
Albarran, G. & Eduard, G. (2009). Data Mining and SQL. Lima, Perú: Megabyte
Baeza-Yates, R. & Ribeiro-Neto, B. (1999). Modern Information Retrieval. Boston, Massachusetts, USA: Addison-Wesley
Belem, F. M.; Almeida, J. M. & Goncalves, M. A. (2017). A Survey on Tag Recommendation Methods. J. Assoc. Inf. Sci. Technol 68(4), 830-844. doi: 10.1002/asi.23736.
Godoy, D. & Amandi, A. Hybrid (2008). Content and Tag-based Profiles for Recommendation in Collaborative Tagging Systems (pp. 58-65). Proceedings of the 2008 Latin American Web Conference. Espírito Santo, Brasil.
Godoy, D. & Corbellini, A. (2016). A State-of-the-art Review. International Journal of Intelligent Systems 31(4), 314–346. doi: 10.1002/int.21753
Gou, Z.; Han, L.; Zhu, J.; Yang, Y. & Duan, B. (2018). Personalized Search by a Multi-Type and Multi-Level User Profile in Folksonomy. Arab. J. Sci. Eng 43(12), 7563–7572. doi: 10.1007/s13369-018-3133-2
Hong, L.; Chi, E. H.; Budiu, R.; Pirolli, P. & Nelson, L. (2008). SparTag.us: A Low Cost Tagging System for Foraging of Web Content. Proceedings of the Working Conference on Advanced Visual Interfaces, 65-72. Napoli, Italy.
Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G. (2006). Information Retrieval in Folksonomies: Search and Ranking. European Semantic Web Conference, 411-426. Budva, Montenegro: Springer.
Ju, S., & Hwang, K.-B. (2009). A weighting scheme for tag recommendation in social bookmarking systems. Proceedings of the 2009th International Conference on ECML PKDD Discovery Challenge - Volume 497, 109–118. https://dl.acm.org/doi/abs/10.5555/3056147.3056156
Levandoski, J. J.; Ekstrand, M. D.; Ludwig, M. J. et al. (2011). Benchmarks for Evaluating Performance of Recommender System Architectures. PVLDB 4, 911-920. doi: 10.14778/3402707.3402729.
Lipczak, M. (2008). Tag Recommendation for Folksonomies Oriented towards Individual Users. Proceedings of ECML PKDD, 84-95. Amberes, Bélgica.
Liu, H. (2018). A Tag-Based Recommender System Framework for Social Bookmarking Websites. Inderscience Publishers 14(3): 303-322. doi: 10.1504/IJWBC.2018.094916
Lo, R. T.; He, B. & Ounis, I. (2005). Automatically Building a Stopword List for an Information Retrieval System. 5th Dutch-Belgium Information Retrieval Workshop (DIR) ’05, 1-8. Países Bajos. https://bit.ly/2Fbz8v5
Mishne, G. (2006). AutoTag: A Collaborative Approach to Automated Tag Assignment for Weblog Posts. Proceedings of the 15th International Conference on World Wide Web, 953-954. Edimburgo, Escocia: ACM.
Musto, C.; Narducci, F.; Gemmis, M. d.; Lops, P., & Semeraro, G. (2009). STaR: A Social Tag Recommender System. Proceedings of the ECML/PKDD Discovery Challenge, 215-227. Bled, Eslovenia.
Peng, J. & Zeng, D. (2010). Making Item Predictions through Tag Recommendations. En: ICEBI2010, 483-490. Kunming, Yunnan, P. R.China: Atlantis Press.
Porter, M. (1980). An Algorithm for Suffix Stripping. Program: electronic library and information systems 14(3): 130–137. doi: 10.1108/eb046814
Portilla Olvera, E. & Godoy, D. (2012). Evaluating Term Weighting Schemes for Content-based Tag Recommendation in Social Tagging Systems. IEEE Latin America Transactions 10(4): 1973-1980. doi: 10.1109/TLA.2012.6272482
Qassimi, S. & Abdelwahed, E. (2019). The Role Of Collaborative Tagging and Ontologies in Emerging Semantic of Web Resources. Computing 101(10): 1489-1511. doi: 10.1007/s00607-019-00704-9
Salton, G., Wong., A. & Yang, C. (1975). A Vector Space Model for Automatic Indexing. Comun. ACM 18(11): 613–620. doi: 10.1145/361219.361220.
Singh, A. K., Nagwani, N. K., & Pandey, S. (2017). TAGme: A Topical Folksonomy Based Collaborative Filtering for Tag Recommendation in Community Sites. Proceedings of the 4th Multidisciplinary International Social Networks Conference, 1–7. https://doi.org/10.1145/3092090.3092095
Sood , S., Owsley, S., Hammond, K. & Birnbaum, L. (2007). Tagassist: Automatic Tag Suggestion for Blog Posts (pp.1-7). International Conference on Weblogs and Social Media,. USA: Boulder, Colorado.
Veerman, E.; Lachev, T. & Sarka, D. (2009). Microsoft SQL Server 2008 - Business Intelligence Development and Maintenance. Washington, EEUU: Microsoft Press.
Wang, K.; Jin, Y.; Wang, H.; Pengi, H. & Wang, X. (2018). Personalized Time-aware Tag Recommendation. Proceedings of the 32nd AAAI Conference on Artificial Intelligence, (pp. 459–466). New Orleans, LA, USA.
Wetzker, R.; Zimmermann, C. & Bauckhage, C. (2008). Analyzing Social Bookmarking Systems: A del.icio.us Cookbook (pp. 26-30). Proceedings of the ECAI 2008 Mining Social Data Workshop. Patras, Grecia.
Wu, Y.; Yao, Y.; Xu, F.;Tong, H. & Lu, J. (2016). Tag2Word: Using tags to generate words for content based tag recommendation. Proceedings of the 2016 ACM Conference on Information and Knowledge Management, 2287–2292. Indianapolis, USA: ACM.
Yu-Ta, L.; Shoou-I, Y.; Tsung-Chieh, C. & Jane Yung-jen, H. (2009). A Content-Based Method to Enhance Tag Recommendation. Proceedings of the 21st international jont conference on Artifical Intelligence, 2064 - 2069. Pasadena, California: Morgan Kaufmann.
Zhang, N.; Zhang, Y. & Tang, J. (2009). A Tag Recommendation System for Folksonomy. Proceedings of the 2nd ACM Workshop on Social Web Search and Mining, 9-16. Hong Kong, China: ACM.
Zhang, Z.-K.; Tao, Z. & Yi-Cheng, Z. (2011). Tag-aware Recommender Systems: A State-of-the-art Survey. J. Comput. Sci. Technol 26(5): 767--777. doi: 10.1007/s11390-011-0176-1
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2020 Enfoque UTE
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The articles and research published by the UTE University are carried out under the Open Access regime in electronic format. This means that all content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, without asking prior permission from the publisher or the author. This is in accordance with the BOAI definition of open access. By submitting an article to any of the scientific journals of the UTE University, the author or authors accept these conditions.
The UTE applies the Creative Commons Attribution (CC-BY) license to articles in its scientific journals. Under this open access license, as an author you agree that anyone may reuse your article in whole or in part for any purpose, free of charge, including commercial purposes. Anyone can copy, distribute or reuse the content as long as the author and original source are correctly cited. This facilitates freedom of reuse and also ensures that content can be extracted without barriers for research needs.
This work is licensed under a Creative Commons Attribution 3.0 International (CC BY 3.0).
The Enfoque UTE journal guarantees and declares that authors always retain all copyrights and full publishing rights without restrictions [© The Author(s)]. Acknowledgment (BY): Any exploitation of the work is allowed, including a commercial purpose, as well as the creation of derivative works, the distribution of which is also allowed without any restriction.
Enfoque UTE - Facultad de Ciencias de la Ingeniería e Industrias - Universidad UTE
