How useful are tags? - An empirical analysis of collaborative tagging for web page recommendation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5075 LNCS, Issue , 2008, Pages 320-330

  Zeng, Daniel ^a,b   Li, Huiqian ^a  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b UNIVERSITY OF ARIZONA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CALCULATIONS; INFORMATION THEORY; INTERSYMBOL INTERFERENCE; RESEARCH; STRUCTURAL PROPERTIES; BEHAVIORAL RESEARCH; COMPLEX NETWORKS; COMPUTATION THEORY; SOCIAL NETWORKING (ONLINE);

(PL) PROPERTIES; COMPLEX NETWORK THEORY; COMPUTATIONAL STUDIES; EMPIRICAL ANALYSES; HEIDELBERG (CO); INTERNATIONAL CONFERENCES; INTERNATIONAL WORKSHOPS; SECURITY INFORMATICS; SIMILARITY CALCULATIONS; SPRINGER (CO); WEB 2.0; WEB PAGES; WEB SEARCHES;

WORLD WIDE WEB; USER INTERFACES;

COLLABORATIVE TAGGING; COMPUTATIONAL STUDIES; EMPIRICAL ANALYSIS; RESEARCH GAPS; SIMILARITY CALCULATION; WEB 2.0 APPLICATIONS; WEB PAGE RECOMMENDATIONS; WEB SEARCHES;

EID: 45849127609     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-69304-8_32     Document Type: Conference Paper

References (12)

1. Huang, Z., Zeng, D.D., Chen, H.: Analyzing Consumer-Product Graphs: Empirical Findings and Applications in Recommender Systems. Management Science 53(7), 1146-1164 (2007)
2. Myung, J., Lee, W., Srivastava, J., Shih, T.K.: Tag-Splitting: Adaptive Collision Arbitration Protocols for RFID Tag Identification. IEEE Transactions on Parallel and Distributed Systems 18(6), 763-775 (2007)
3. Golder, S., Huberman, B.A.: The Structure of Collaborative Tagging System (2005)
4. Halpin, H., Robe, V., Shepherd, H.: The Complex Dynamics of Collaborative Tagging. In: Proceeding of the 16th international conference on World Wide Web, Banff, Alberta, Canada, ACM Press, New York (2007)
5. Ames, M., Naaman, M.: Why we tag: the motivations for annotation in mobile and online media. In: Proceedings of the SIGCHI conference on Human factors in computing systems, San Jose, California, USA, pp. 971-980. ACM Press, New York (2007)
6. Brooks, C.H., Nancy, M.: Improved annotation of the blogosphere via autotagging hierarchical clustering. In: Proceedings of the 15th international conference on World Wide Web, Edinburgh, Scotland, pp. 625-632. ACM Press, New York (2006)
7. Yanbe, Y., Jatowt, A., Nakamura, S., Tanaka, K.: Can social bookmarking enhance search in the web? In: Proceedings of the 2007 conference on Digital Libraries, Vancouver, BC, Canada, pp. 107-116. ACM Press, New York (2007)
8. Li., R., Bao, S., Yong, Y., Fei, B., Su, Z.: Toward effective browing of large scale social annotations. In: Proceedings of the 16th international conference on World Wide Web, Banff, Alberta, Canada, pp. 943-952. ACM Press, New York (2007)
9. Wu, X., Zhang, L., Yu, Y.: Exploring social annotations for the semantic web. In: Proceedings of the 15th international conference on World Wide Web, Edinburgh, Scotland, pp. 417-426. ACM Press, New York (2006)
10. Bao, S., Xue, G., Wu, X., Yu, Y., Fei, B., Shu, Z.: Optimizing web search using social annotation. In: Proceedings of the 16th international conference on World Wide Web, Banff, Alberta, Canada, pp. 501-510. ACM Press, New York (2007)
11. Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proc. Fourteenth Conf. Uncertainty Artificial Intelligence, Madison, WI, pp. 43-52 (1998)
12. Robertson, S.: Understand inverse document frequency: on theoretical argument of IDF. Journal of Documentation 60(5), 503-520 (2004)