Marek Lipczak
ABSTRACT
The objective of a tag recommendation system is to propose a set of tags for a resource to ease the tagging process done manually by a user. Tag recommendation is an interesting and well defined research problem. However, while solving it, it is easy to forget about its practical implications. We discuss the practical aspects of tag recommendation and propose a system that successfully addresses the problem of learning in tag recommendation, without sacrificing efficiency. Learning is realized in two aspects: adaptation to newly added posts and parameter tuning. The content of each added post is used to update the resource and user profiles as well as associations between tags. Parameter tuning allows the system to automatically adjust the way tag sources (e.g., content related tags or user profile tags) are combined to match the characteristics of a specific collaborative tagging system. The evaluation on data from three collaborative tagging systems confirmed the importance of both learning methods. Finally, an architecture based on text indexing makes the system efficient enough to serve in real time collaborative tagging systems with number of posts counted in millions, given limited computing resources.
Supplemental Material
- }}M. Bender, T. Crecelius, M. Kacimi, S. Michel, T. Neumann, J. X. Parreira, R. Schenkel, and G. Weikum. Exploiting social relations for query expansion and result ranking. In Data Engineering for Blogs, Social Media, and Web 2.0, ICDE 2008 Workshops, pages 501--506, 2008.Google Scholar
- }}P. R. Cohen and R. Kjeldsen. Information retrieval by constrained spreading activation in semantic networks. Inf. Process. Manage., 23(4):255--268, 1987. Google ScholarDigital Library
- }}F. Eisterlehner, A. Hotho, and R. Jäschke, editors. ECML PKDD Discovery Challenge 2009 (DC09), volume 497 of CEUR-WS.org, 2009.Google Scholar
- }}J. Gemmell, M. Ramezani, T. Schimoler, L. Christiansen, and B. Mobasher. The impact of ambiguity and redundancy on tag recommendation in folksonomies. In RecSys '09: Proc. the Third ACM Conference on Recommender Systems, pages 45--52. ACM, 2009. Google ScholarDigital Library
- }}S. A. Golder and B. A. Huberman. Usage patterns of collaborative tagging systems. J. Inf. Sci., 32(2):198--208, 2006. Google ScholarDigital Library
- }}A. Hotho, R. Jäschke, C. Schmitz, and G. Stumme. Trend detection in folksonomies. Semantic Multimedia, pages 56--70, 2006. Google ScholarDigital Library
- }}R. Jäschke, L. Marinho, A. Hotho, L. Schmidt-Thieme, and G. Stumme. Tag recommendations in folksonomies. Knowledge Discovery in Databases: PKDD 2007, pages 506--514, 2007. Google ScholarDigital Library
- }}S. Ju and K.-B. Hwang. A weighting scheme for tag recommendation in social bookmarking systems. In Proc. the ECML/PKDD 2009 Discovery Challenge Workshop, pages 109--118, 2009.Google Scholar
- }}R. Krestel, P. Fankhauser, and W. Nejdl. Latent dirichlet allocation for tag recommendation. In RecSys '09: Proc. the Third ACM Conference on Recommender Systems, pages 61--68. ACM, 2009. Google ScholarDigital Library
- }}M. Lipczak, Y. Hu, Y. Kollet, and E. Milios. Tag sources for recommendation in collaborative tagging systems. In Proc. the ECML/PKDD 2009 Discovery Challenge Workshop, pages 157--172, 2009.Google Scholar
- }}M. Lipczak and E. Milios. The impact of resource title on tags in collaborative tagging systems. In HT'10: Proc. the 21th ACM Conference on Hypertext and Hypermedia, pages 179--188. ACM, 2010. Google ScholarDigital Library
- }}C. Musto, F. Narducci, M. de Gemmis, P. Lops, and G. Semeraro. STaR: a social tag recommender system. In Proc. the ECML/PKDD 2009 Discovery Challenge Workshop, pages 215--227, 2009.Google Scholar
- }}S. Rendle, L. B. Marinho, A. Nanopoulos, and L. Schmidt-Thieme. Learning optimal ranking with tensor factorization for tag recommendation. In KDD '09: Proc. the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 727--736. ACM, 2009. Google ScholarDigital Library
- }}P. Symeonidis, A. Nanopoulos, and Y. Manolopoulos. Tag recommendations based on tensor dimensionality reduction. In RecSys '08: Proc. the 2008 ACM Conference on Recommender Systems, pages 43--50. ACM, 2008. Google ScholarDigital Library
- }}M. Tatu, M. Srikanth, and T. D'Silva. Rsdc'08: Tag recommendations using bookmark content. In Proc. the ECML/PKDD 2008 Discovery Challenge Workshop, pages 96--107, 2008.Google Scholar
- }}R. Wetzker, C. Zimmermann, and C. Bauckhage. Analyzing social bookmarking systems: A del.icio.us cookbook. In Mining Social Data (MSoDa) Workshop Proceedings, pages 26--30, 2008.Google Scholar
Index Terms
- Learning in efficient tag recommendation
Recommendations
Flickr tag recommendation based on collective knowledge
WWW '08: Proceedings of the 17th international conference on World Wide WebOnline photo services such as Flickr and Zooomr allow users to share their photos with family, friends, and the online community at large. An important facet of these services is that users manually annotate their photos using so called tags, which ...
Efficient Tag Recommendation for Real-Life Data
Despite all of the advantages of tags as an easy and flexible information management approach, tagging is a cumbersome task. A set of descriptive tags has to be manually entered by users whenever they post a resource. This process can be simplified by ...
Content-based and collaborative techniques for tag recommendation: an empirical evaluation
The rapid growth of the so-called Web 2.0 has changed the surfers' behavior. A new democratic vision emerged, in which users can actively contribute to the evolution of the Web by producing new content or enriching the existing one with user generated ...
Comments