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Cross-tagging for personalized open social networking

Published: 29 June 2009 Publication History

Abstract

The Social Web is successfully established and poised for continued growth. Web 2.0 applications such as blogs, bookmarking, music, photo and video sharing systems are among the most popular; and all of them incorporate a social aspect, i.e., users can easily share information with other users. But due to the diversity of these applications -- serving different aims -- the Social Web is ironically divided. Blog users who write about music for example, could possibly benefit from other users registered in other social systems operating within the same domain, such as a social radio station. Although these sites are two different and disconnected systems, offering distinct services to the users, the fact that domains are compatible could benefit users from both systems with interesting and multi-faceted information. In this paper we propose to automatically establish social links between distinct social systems through cross-tagging, i.e., enriching a social system with the tags of other similar social system(s). Since tags are known for increasing the prediction quality of recommender systems (RS), we propose to quantitatively evaluate the extent to which users can benefit from cross-tagging by measuring the impact of different cross-tagging approaches on tag-aware RS for personalized resource recommendations. We conduct experiments in real world data sets and empirically show the effectiveness of our approaches.

References

[1]
]]K. Bischoff, C. S. Firan, W. Nejdl, and R. Paiu. Can all tags be used for search? In CIKM '08: Proceedings of the 17th Conference on Information and Knowledge Management. To Appear. ACM, 2008.
[2]
]]M. Braun, K. Dellschaft, T. Franz, D. Hering, P. Jungen, H. Metzler, E. Muller, A. Rostilov, and C. Saathoff. Personalized search and exploration with mytag. In WWW '08: Proceeding of the 17th international conference on World Wide Web, pages 1031--1032, New York, NY, USA, 2008. ACM.
[3]
]]J. S. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI-98), pages 43--52. Morgan Kaufmann, 1998.
[4]
]]C. S. Firan, W. Nejdl, and R. Paiu. The benefit of using tag-based profiles. In LA-WEB '07: Proceedings of the 2007 Latin American Web Conference, pages 32--41, Washington, DC, USA, 2007. IEEE Computer Society.
[5]
]]C. Hanser and B. Berendt. Tags are not metadata, but "just more content" - to some people. In Proceedings of the International Conference on Weblogs and Social Media (ICWSM 2007), 2007.
[6]
]]R. Jaschke, A. Hotho, C. Schmitz, B. Ganter, and G. Stumme. Discovering shared conceptualizations in folksonomies. Web Semant., 6(1):38--53, 2008.
[7]
]]R. Jaschke, L. Marinho, A. Hotho, L. Schmidt-Thieme, and G. Stumme. Tag recommendations in social bookmarking systems. AI Communications, pages 231--247, 2008.
[8]
]]T. G. Kolda and B. W. Bader. Tensor decompositions and applications. SIAM Review. to appear (accepted June 2008).
[9]
]]T. G. Kolda and J. Sun. Scalable tensor decompositions for multi-aspect data mining. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), December 2008.
[10]
]]L. B. Marinho, K. Buza, and L. Schmidt-Thieme. Folksonomy-based collabulary learning. In International Semantic Web Conference (ISWC 08). Springer, 2008.
[11]
]]L. B. Marinho and L. Schmidt-Thieme. Collaborative tag recommendations. In Proceedings of 31st Annual Conference of the Gesellschaft fur Klassifikation (GfKl), Freiburg. Springer, 2007.
[12]
]]C. McCarthy. Myspace announces "Data Availability" project with yahoo, ebay, photobucket, twitter. http://news.cnet.com/8301-13577 3-9939286-36.html, 2008.
[13]
]]B. Mehta and T. Hofmann. Cross system personalization by learning manifold alignments. In KI 2006: Advances in Artificial Intelligence, volume 4314/2007, pages 244--259. Springer Berlin / Heidelberg, 2006.
[14]
]]B. Mehta, T. Hofmann, and P. Fankhauser. Cross system personalization by factor analysis. In ITWP Workshop at AAAI 2006. AAAI Press, 2006.
[15]
]]D. Morin. Announcing facebook connect. http://developers.facebook.com/news.php?blog=1&story=108, 2008.
[16]
]]C. Niederee, A. Stewart, B. Mehta, and M. Hemmje. A multi-dimensional, unified user model for cross-system personalization. In Proceedings of Workshop On Environments For Personalized Information Access at Advanced Visual Interfaces, May 2004.
[17]
]]S. Oldenburg. Comparative studies of social classification systems using rss feeds. In J. Cordeiro, J. Filipe, and S. Hammoudi, editors, WEBIST (2), pages 394--403. INSTICC Press, 2008.
[18]
]]R. Paiu, L. Chen, C. S. Firan, and W. Nejdl. Pharos - personalizing users' experience in audio-visual online spaces. In PersDB, pages 40--47, 2008.
[19]
]]P. Resnick, N. Iacovou, M. Suchak, P. Bergstorm, and J. Riedl. Grouplens: An open architecture for collaborative filtering of netnews. In Proc. of ACM 1994 Conference on Computer Supported Cooperative Work, pages 175--186, Chapel Hill, North Carolina, 1994. ACM.
[20]
]]Shankara Bhargava Subramanya. View Completation And Collaborative Tagging In Blogosphere. Master's thesis, Arizona State University, July 2008.
[21]
]]M. Y. Symeonidis P., Nanopoulos A. Tag recommendations based on tensor dimensionality reduction. In 2nd ACM Conference in Recommender Systems (RecSys 08), pages 43--50, Lausanne, Switzerland, 2008.
[22]
]]M. Szomszor, H. Alani, I. Cantador, K. O'Hara, and N. Shadbolt. Semantic modelling of user interests based on cross-folksonomy analysis. In International Semantic Web Conference, volume 5318 of Lecture Notes in Computer Science, pages 632--648. Springer, 2008.
[23]
]]M. Szomszor, H. Alani, I. Cantador, K. O'Hara, and N. Shadbolt. Semantic modelling of user interests based on cross-folksonomy analysis. In International Semantic Web Conference, pages 632--648, 2008.
[24]
]]K. H. L. Tso-Sutter, L. B. Marinho, and L. Schmidt-Thieme. Tag-aware recommender systems by fusion of collaborative filtering algorithms. In SAC'08: Proceedings of the 2008 ACM symposium on Applied computing, pages 1995--1999, New York, NY, USA, 2008. ACM.
[25]
]]C. Wang, Y. Zhang, and F. Zhang. User modeling for cross system personalization in digital libraries. Information Technologies and Applications in Education, 2007. ISITAE '07. First IEEE International Symposium on, pages 238--243, Nov. 2007.
[26]
]]J. Wang and B. D. Davison. Explorations in tag suggestion and query expansion. In SSM '08: Proceeding of the 2008 ACM workshop on Search in social media, pages 43--50, New York, NY, USA, 2008. ACM.

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      cover image ACM Conferences
      HT '09: Proceedings of the 20th ACM conference on Hypertext and hypermedia
      June 2009
      410 pages
      ISBN:9781605584867
      DOI:10.1145/1557914
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      Published: 29 June 2009

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      Author Tags

      1. recommender systems
      2. social media
      3. web 2.0

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      • (2021)Deep sparse autoencoder prediction model based on adversarial learning for cross-domain recommendationsKnowledge-Based Systems10.1016/j.knosys.2021.106948220(106948)Online publication date: May-2021
      • (2019)Addressing the user cold start with cross-domain collaborative filteringUser Modeling and User-Adapted Interaction10.1007/s11257-018-9217-629:2(443-486)Online publication date: 1-Apr-2019
      • (2016)Folksonomy-Based Recommender SystemsInternational Journal of Intelligent Systems10.1002/int.2175331:4(314-346)Online publication date: 1-Apr-2016
      • (2015)Cross-Domain Recommender SystemsRecommender Systems Handbook10.1007/978-1-4899-7637-6_27(919-959)Online publication date: 2015
      • (2014)Analyzing user behavior across social sharing environmentsACM Transactions on Intelligent Systems and Technology10.1145/25355265:1(1-31)Online publication date: 3-Jan-2014
      • (2014)Web data extraction, applications and techniquesKnowledge-Based Systems10.1016/j.knosys.2014.07.00770:C(301-323)Online publication date: 1-Nov-2014
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      • (2012)An approach to deriving a virtual thematic folksonomy based system from a social inter-folksonomy based scenarioWeb Intelligence and Agent Systems10.5555/2590069.259007010:4(361-384)Online publication date: 1-Oct-2012
      • (2012)Design and Evaluation of Cross-Domain Recommender SystemsRecommender Systems Handbook10.1007/978-1-0716-2197-4_13(485-516)Online publication date: 24-Feb-2012
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