skip to main content
research-article

The task-dependent effect of tags and ratings on social media access

Published: 23 November 2010 Publication History

Abstract

Recently, online social networks have emerged that allow people to share their multimedia files, retrieve interesting content, and discover like-minded people. These systems often provide the possibility to annotate the content with tags and ratings.
Using a random walk through the social annotation graph, we have combined these annotations into a retrieval model that effectively balances the personal preferences and opinions of like-minded users into a single relevance ranking for either content, tags, or people. We use this model to identify the influence of different annotation methods and system design aspects on common ranking tasks in social content systems.
Our results show that a combination of rating and tagging information can improve tasks like search and recommendation. The optimal influence of both sources on the ranking is highly dependent on the retrieval task and system design. Results on content search and tag suggestion indicate that the profile created by a user's annotations can be used effectively to adapt the ranking to personal preferences. The random walk reduces sparsity problems by smoothly integrating indirectly related concepts in the relevance ranking, which is especially valuable for cold-start users or individual tagging systems like YouTube and Flickr.

References

[1]
Amer-Yahia, S., Benedikt, M., and Bohannon, P. 2007. Challenges in searching online communities. IEEE Data Eng. Bull. 30, 2, 23--31.
[2]
Bao, S., Xue, G., Wu, X., Yu, Y., Fei, B., and Su, Z. 2007. Optimizing Web search using social annotations. In Proceedings of the 16th International Conference on World Wide Web (WWW). ACM Press, New York, NY, 501--510.
[3]
Barabási, A.-L. 2005. The origin of bursts and heavy tails in human dynamics. Nature 435, 7039, 207--211.
[4]
Begelman, G., Keller, P., and Smadja, F. 2006. Automated tag clustering: Improving search and exploration in the tag space. In Proceedings of the Collaborative Web Tagging Workshop (WWW).
[5]
Breese, J. S., Heckerman, D., and Kadie, C. 1998. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI). Morgan Kaufmann, San Francisco, CA, 43--52.
[6]
Clements, M., de Vries, A., and Reinders, M. J. T. 2009a. Exploiting positive and negative graded relevance assessments for content recommendation. In Proceedings of the 6th International Workshop on Algorithms and Models for the Web-Graph (WWW). Springer-Verlag, Berlin, 155--166.
[7]
Clements, M., de Vries, A. P., and Reinders, M. J. T. 2008. Detecting synonyms in social tagging systems to improve content retrieval. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). ACM, New York, NY, 739--740.
[8]
Clements, M., de Vries, A. P., and Reinders, M. J. T. 2009b. The influence of personalization on tag query length in social media search. Inform. Process. Manag.
[9]
Craswell, N. and Szummer, M. 2007. Random walks on the click graph. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, New York, NY, 239--246.
[10]
Fouss, F., Pirotte, A., Renders, J.-M., and Saerens, M. 2007. Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Trans. Knowl. Data Eng. 19, 3, 355--369.
[11]
Funk, S. 2006. http://sifter.org/~simon/journal/20061211.html.
[12]
Furnas, G. W., Landauer, T. K., Gomez, L. M., and Dumais, S. T. 1987. The vocabulary problem in human-system communication. Comm. ACM 30, 11, 964--971.
[13]
Goldberg, K., Roeder, T., Gupta, D., and Perkins, C. 2001. Eigentaste: A constant time collaborative filtering algorithm. Inform. Retr. 4, 2, 133--151.
[14]
Golder, S. A. and Huberman, B. A. 2006. Usage patterns of collaborative tagging systems. J. Inform. Sci. 32, 2, 198--208.
[15]
Halpin, H., Robu, V., and Shepherd, H. 2007. The complex dynamics of collaborative tagging. In Proceedings of the 16th International Conference on World Wide Web (WWW). ACM, New York, NY, 211--220.
[16]
Herlocker, J., Konstan, J. A., and Riedl, J. 2002. An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Inform. Retr. 5, 4, 287--310.
[17]
Herlocker, J. L. and Konstan, J. A. 2001. Content-independent task-focused recommendation. IEEE Internet Comput. 5, 6, 40--47.
[18]
Hotho, A., Jäschke, R., Schmitz, C., and Stumme, G. 2006. Information retrieval in folksonomies: Search and ranking. In Proceedings of the Extended Semantic Web Conference (ESWC). Lecture Notes in Computer Science, vol. 4011, Springer-Verlag, Berlin, 411--426.
[19]
Huang, Z., Chen, H., and Zeng, D. 2004. Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Trans. Inform. Syst. 22, 1, 116--142.
[20]
Järvelin, K. and Kekäläinen, J. 2002. Cumulated gain-based evaluation of IR techniques. ACM Trans. Inform. Syst. 20, 4, 422--446.
[21]
Jäschke, R., Marinho, L., Hotho, A., Schmidt-Thieme, L., and Stumme, G. 2007. Tag recommendations in folksonomies. In Proceedings of the Conferenece on Knowledge Discovery in Databases (PKDD). Lecture Notes in Computer Science, vol. 4702, Springer Berlin, 506--514.
[22]
Kaser, O. and Lemire, D. 2007. Tag-cloud drawing: Algorithms for cloud visualization. In Proceedings of the Tagging and Metadata for Social Information Organization Workshop (WWW).
[23]
Lambiotte, R. and Ausloos, M. 2006. Collaborative tagging as a tripartite network. In Proceedings of the International Conference on Computational Science (ICCS). Lecture Notes in Computer Science, vol. 3993, 1114--1117.
[24]
Liben-Nowell, D. and Kleinberg, J. 2003. The link prediction problem for social networks. In Proceedings of the 12th International Conference on Information and knowledge Management (CIKM). ACM, New York, NY, 556--559.
[25]
Lipczak, M. 2008. Tag recommendation for folksonomies oriented towards individual users. In Proceedings of European Conference on Machine Learning and Practice of Knowledge Discovery in Databases (ECML PKDD) Discovery Challenge (RSDC08). 84--95.
[26]
Liu, N. N. and Yang, Q. 2008. Eigenrank: A ranking-oriented approach to collaborative filtering. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). ACM, New York, NY, 83--90.
[27]
Marlow, C., Naaman, M., Boyd, D., and Davis, M. 2006. Ht06, tagging paper, taxonomy, flickr, academic article, to read. In Proceedings of the 17th conference on Hypertext and Hypermedia (HYPERTEXT). ACM Press, New York, NY, 31--40.
[28]
Mika, P. 2005. Ontologies are us: A unified model of social networks and semantics. Y. Gil, E. Motta, R. V. Benjamins, and M. Musen, Eds. Lecture Notes in Computer Science, vol. 3729, 522--536.
[29]
Mirza, B. J., Keller, B. J., and Ramakrishnan, N. 2003. Studying recommendation algorithms by graph analysis. J. Intell. Inform. Syst. 20, 2, 131--160.
[30]
Page, L., Brin, S., Motwani, R., and Winograd, T. 1998. The Pagerank citation ranking: Bringing order to the Web. Tech. rep., Stanford Digital Library Technologies Project.
[31]
Ramakrishnan, N., Keller, B. J., Mirza, B. J., Grama, A. Y., and Karypis, G. 2001. Privacy risks in recommender systems. IEEE Internet Comput. 5, 6, 54--62.
[32]
Resnick, P. and Varian, H. R. 1997. Recommender systems. Comm. ACM 40, 3, 56--58.
[33]
Salton, G. and Buckley, C. 1988. Term-weighting approaches in automatic text retrieval. Inform. Proc. Manag. 24, 5, 513--523.
[34]
Sarwar, B., Karypis, G., Konstan, J., and Riedl, J. 2000. Application of dimensionality reduction in recommender systems—a case study. In Proceedings of the ACM WebKDD Workshop.
[35]
Schenkel, R., Crecelius, T., Kacimi, M., Michel, S., Neumann, T., Parreira, J. X., and Weikum, G. 2008. Efficient top-k querying over social-tagging networks. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, New York, NY, 523--530.
[36]
Sen, S., Lam, S. K., Rashid, A. M., Cosley, D., Frankowski, D., Osterhouse, J., Harper, F. M., and Riedl, J. 2006. Tagging, communities, vocabulary, evolution. In Proceedings of the 20th Anniversary Conference on Computer Supported Cooperative Work (CSCW). ACM, New York, NY, 181--190.
[37]
Smyth, B. and Balfe, E. 2006. Anonymous personalization in collaborative Web search. Inform. Retrieval 9, 2, 165--190.
[38]
Song, Y., Zhuang, Z., Li, H., Zhao, Q., Li, J., Lee, W. C., and Giles, C. L. 2008. Real-time automatic tag recommendation. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, New York, NY, 515--522.
[39]
Symeonidis, P., Nanopoulos, A., and Manolopoulos, Y. 2008. Tag recommendations based on tensor dimensionality reduction. In Proceedings of the ACM Conference on Recommender Systems (RecSys). ACM, New York, NY, 43--50.
[40]
Szummer, M. and Jaakkola, T. 2001. Partially labeled classification with Markov random walks. In Advances in Neural Information Processing Systems (NIPS). Vol. 14. MIT Press, 945--952.
[41]
Tatu, M., Srikanth, M., and D'Silva, T. 2008. Tag recommendations using bookmark content. In Proceedings of ECML PKDD Discovery Challenge (RSDC). 96--107.
[42]
Vander Wal, T. 2005. Explaining and showing broad and narrow folksonomies. http://www.vanderwal.net/random/entrysel.php?blog=1635.
[43]
Wang, J., de Vries, A. P., and Reinders, M. J. T. 2006a. Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM Press, New York, NY, 501--508.
[44]
Wang, X., Sun, J.-T., Chen, Z., and Zhai, C. 2006b. Latent semantic analysis for multiple-type interrelated data objects. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM Press, New York, NY, 236--243.
[45]
Wilcoxon, F. 1945. Individual comparisons by ranking methods. Biometrics Bull. 1, 6, 80--83.
[46]
Xi, W., Fox, E. A., Fan, W., Zhang, B., Chen, Z., Yan, J., and Zhuang, D. 2005. Simfusion: measuring similarity using unified relationship matrix. In Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, New York, NY, 130--137.
[47]
Xu, Z., Fu, Y., Mao, J., and Su, D. 2006. Towards the semantic Web: Collaborative tag suggestions. In Proceedings of the Collaborative Web Tagging Workshop (WWW). Edinburgh, Scotland.

Cited By

View all
  • (2023)Improving edit-based unsupervised sentence simplification using fine-tuned BERTPattern Recognition Letters10.1016/j.patrec.2023.01.009166:C(112-118)Online publication date: 1-Feb-2023
  • (2023)A multimodal hyperlapse method based on video and songs’ emotion alignmentPattern Recognition Letters10.1016/j.patrec.2022.08.014166:C(174-181)Online publication date: 1-Feb-2023
  • (2020)Learning Semantic Representations from Directed Social Links to Tag Microblog Users at ScaleACM Transactions on Information Systems10.1145/337755038:2(1-30)Online publication date: 7-Mar-2020
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Information Systems
ACM Transactions on Information Systems  Volume 28, Issue 4
November 2010
204 pages
ISSN:1046-8188
EISSN:1558-2868
DOI:10.1145/1852102
Issue’s Table of Contents
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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 November 2010
Accepted: 01 December 2009
Revised: 01 July 2009
Received: 01 November 2008
Published in TOIS Volume 28, Issue 4

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Personalization
  2. content retrieval
  3. librarything
  4. movielens
  5. random walk
  6. rating
  7. recommendation
  8. social media
  9. tagging

Qualifiers

  • Research-article
  • Research
  • Refereed

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)16
  • Downloads (Last 6 weeks)0
Reflects downloads up to 15 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2023)Improving edit-based unsupervised sentence simplification using fine-tuned BERTPattern Recognition Letters10.1016/j.patrec.2023.01.009166:C(112-118)Online publication date: 1-Feb-2023
  • (2023)A multimodal hyperlapse method based on video and songs’ emotion alignmentPattern Recognition Letters10.1016/j.patrec.2022.08.014166:C(174-181)Online publication date: 1-Feb-2023
  • (2020)Learning Semantic Representations from Directed Social Links to Tag Microblog Users at ScaleACM Transactions on Information Systems10.1145/337755038:2(1-30)Online publication date: 7-Mar-2020
  • (2020)Graph Databases for Information RetrievalAdvances in Information Retrieval10.1007/978-3-030-45442-5_79(608-612)Online publication date: 14-Apr-2020
  • (2020)Semantic Path-Based Learning for Review Volume PredictionAdvances in Information Retrieval10.1007/978-3-030-45439-5_54(821-835)Online publication date: 14-Apr-2020
  • (2018)Random Walk with Restart for Automatic Playlist Continuation and Query-Specific AdaptationsProceedings of the ACM Recommender Systems Challenge 201810.1145/3267471.3267483(1-6)Online publication date: 2-Oct-2018
  • (2018)Tag-Based RecommendationSocial Information Access10.1007/978-3-319-90092-6_12(441-479)Online publication date: 3-May-2018
  • (2017)Multimodal Classification of Violent Online Political Extremism Content with Graph Convolutional NetworksProceedings of the on Thematic Workshops of ACM Multimedia 201710.1145/3126686.3126776(245-252)Online publication date: 23-Oct-2017
  • (2017)A faceted approach to reachability analysis of graph modelled collectionsInternational Journal of Multimedia Information Retrieval10.1007/s13735-017-0145-87:3(157-171)Online publication date: 16-Dec-2017
  • (2017)Markov Random Walk vs. Higher-Order Factorization Machines: A Comparison of State-of-the-Art Recommender AlgorithmsInnovations for Community Services10.1007/978-3-319-60447-3_7(87-103)Online publication date: 2-Jun-2017
  • Show More Cited By

View Options

Login options

Full Access

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media